So the rep makes something up. Or hedges. Or punts to follow-up email. Either way, the deal slips.

The problem isn’t that your team needs better talkers. It’s that competitive intel built on memory and vibes can’t survive contact with a live sales call. Sales reps need a document they can scan in 30 seconds, with sourced claims they can quote, and counter-positioning scripts that hold up when the buyer pushes back.

When battlecards are systematic, every competitive deal sharpens. The rep knows when to lean in (their three “when we win” scenarios). They know when to walk away (the “red flags” qualification-out criteria). They know which discovery questions expose the competitor’s weaknesses without sounding like a smear. And every claim traces back to a verified source — so the rep isn’t making it up, they’re quoting it.

This is the skill I run when a client’s sales team starts losing competitive deals to one specific competitor — or when a new entrant shows up in the pipeline and nobody knows how to talk about them. It produces a structured 10-section battlecard per competitor, with confidence levels on every claim and explicit [CONFIRM] markers where data is missing.

How it works — visual identity extraction

The skill runs in four phases. Phase 1 confirms the scope (single competitor or batch). Phase 2 pulls intel from upstream skills (competitor-research, win-loss-analysis, product-messaging) and live web research. Phase 3 generates the 10-section battlecard with confidence-tagged claims. Phase 4 validates sources and formats for delivery.

Why screenshots instead of scraping CSS? Because what you *see* is the source of truth. CSS gets compiled, overridden, conditionally loaded. A Framer site injects styles dynamically. A Tailwind build compiles utility classes away. But a screenshot of the live homepage shows you exactly what a visitor sees — the real colors, the actual spacing, the true typography.

Phase 1: Scope confirmation

Before anything else, the skill asks which competitor(s). Single competitor for a deep, deal-specific battlecard. Batch mode (3-5 competitors) when sales needs a competitive landscape view.

It also confirms what intel already exists. Has competitor-research been run on this competitor? Is there a win-loss-analysis output with patterns from real deals? Is there a product-messaging doc with your value props? If yes, the skill inherits those. If no, it triggers them upstream — battlecards are not a substitute for competitor research, they’re the application layer on top of it.

Phase 2: Intel gathering

This is where the skill separates from a one-pager exercise. It pulls from four sources:

→ competitor-research output — the company facts, positioning, feature set, and pricing model already collected → win-loss-analysis output — the actual patterns from won and lost deals (which scenarios you win, which you lose, what objections come up) → product-messaging output — your value props, so counter-positioning isn’t generic but anchored to your actual differentiators → Live web research — the competitor’s current homepage, pricing page, customer logos, recent funding announcements (timestamped to the access date)

Every fact gets a confidence level: High (direct from official source — pricing page, press release, case study), Medium (third-party with multiple signals — G2, news article), Low (single indirect source — forum mention, employee LinkedIn post), or UNVERIFIED (placeholder for the team to fill in).

Phase 3: The 10-section battlecard

Every battlecard has these sections, in this order, no exceptions:

1. Quick facts — Founded, funding, employees, target market, pricing model, starting price (side-by-side table with your company)

2. When we win — 3 specific scenarios + verbatim customer proof point

3. When we lose — 2+ honest losing scenarios + mitigation actions

4. Their pitch — Verbatim competitor language pulled from their homepage and sales material

5. Counter-positioning — Scripted “when they say X, we say Y” responses, tied to your messaging

6. Feature comparison — Honest side-by-side table with a winner-per-row column

7. Landmines to plant — 3-5 discovery questions that expose competitor weaknesses

8. Objection handling — Common objections with proof-pointed responses

9. Red flags — Qualification-out criteria for when to walk away

10. Sources — Every claim with URL, access date, and confidence

The “When we lose” section is the one most teams skip. Naming the scenarios where the competitor genuinely beats you builds credibility with sales — it tells reps “here’s the deal you should disqualify, not white-knuckle through.” The mitigation actions are what separate a real battlecard from a marketing brochure.

The “Landmines to plant” section is the part that actually shifts deals. These aren’t claims about the competitor — they’re discovery questions the rep asks the buyer that expose the competitor’s weaknesses through the buyer’s own answers. “How important is real-time collaboration on long-form content for your team?” lands very differently than “We’re better than Competitor X at collaboration.” One is a sales technique. The other is marketing copy in a sales call.

Phase 4: Anti-hallucination layer

Sales credibility dies the first time a rep quotes invented competitor pricing in a deal and the buyer fact-checks it on the call. The skill enforces three guardrails:

→ Never invent competitor pricing. If the pricing page is gated, the skill marks it [UNAVAILABLE — request demo or check Sales Nav] and notes where to find it. Never a guess. → Never fabricate customer quotes. Verbatim only or [PLACEHOLDER: need customer quote — pull from G2 or call recording]. → Confidence levels on everything. A competitor’s stated headcount from their LinkedIn page is High. An estimate from “they look about that size” is UNVERIFIED.

The result is a battlecard a rep can hand to a buyer mid-call without flinching. Every claim has a source. Every gap is named. Nothing is invented.

See it in action

Setup: You’re at Sanity, the structured-content platform built around an all-code backend. You’ve started losing late-stage deals to Hygraph, the GraphQL-native federated content platform. Both companies serve technical buyers in the same headless-CMS bracket. Both publish detailed positioning. The sales team needs a battlecard that explains the difference without sliding into “we’re better.”

Inputs to the skill: → competitor-research output on Hygraph (run the prior week) → Recent win-loss notes from three deals (one won, two lost) with verbatim buyer language → Sanity’s product-messaging library with current value props

What the skill surfaces in the battlecard:

Their pitch (Section 4, verbatim from hygraph.com homepage):

“Federated content platform. GraphQL-native. Build content APIs without compromise.”

Hygraph’s positioning anchor is federation — content from multiple sources unified at the API layer. This is the language the buyer is hearing in the demo. The rep needs to acknowledge it cleanly, not pretend it doesn’t exist.

Counter-positioning (Section 5):

When they say: “We need a federated content layer to unify our existing content sources.” We say: “Federation works when your content already lives in clean structured systems. If you’re starting fresh, the question is what shape your content should take — and that’s a structured-content question, not a federation question. We start with the schema.”

This isn’t a feature comparison. It’s a re-frame. The rep is shifting the buyer’s evaluation criteria from “which platform federates better” to “which platform makes the content modeling decision easier.” That’s the move.

Landmines to plant (Section 7):

→ “What does your current content modeling process look like? How long does a new content type take to ship today?” → “How comfortable is your team writing GraphQL queries directly versus using a typed SDK?” → “When you say ‘federated content,’ which sources are you actually planning to federate — and are those sources already producing structured content?”

The first question surfaces whether the buyer has a real modeling problem. The second question exposes the GraphQL learning curve Hygraph requires. The third question forces the buyer to articulate whether federation solves a real problem they have, or whether it’s a feature they’re attracted to in the abstract.

Red flags (Section 9):

→ Buyer’s primary use case is “we have content scattered across 6+ legacy systems and need an API layer.” Walk away — this is genuinely Hygraph’s territory. → Buyer has zero developer capacity for schema design. Walk away — Sanity’s all-code backend assumes a developer in the loop.

Honest qualification-out criteria. The rep doesn’t fight every deal — they fight the right deals.

Output: A 10-section battlecard with every claim sourced (homepage URL + access date + confidence level), three planted landmines, two honest “when we lose” scenarios, and counter-positioning scripts tied to Sanity’s messaging library. Sales scans it in 30 seconds before the call. The deal doesn’t slip on a question the rep couldn’t answer.

When to use it

→ A specific competitor keeps showing up in late-stage losses. You’ve seen the same name in four lost deals this quarter. The pattern is real. Build a battlecard so the next rep walking into that deal has a structured response, not improvisation.

→ A new competitor enters the market or your pipeline. The buyer mentions a name your team has never heard. Sales is making it up on the call. Build the battlecard before the next deal hits the same buyer-mention stage.

→ Your team launches a feature that overlaps with an incumbent’s positioning. The launch is going to surface comparison questions in every sales call for the next quarter. Battlecards turn that comparison from a stumbling block into a re-framing opportunity.

Get the Skill

Want to run this yourself? The full skill — 10-section template, confidence framework, batch generation, anti-hallucination guardrails, and Google Docs export — is open source:

Get the skill on GitHub

Save the SKILL.md to your .claude/skills/ folder, then run /battlecards in Claude Code. The skill handles intel pulls from upstream research, structures the battlecard, and tags every claim with a confidence level.

This is one of 100+ GTM skills I’ve built in Claude Code to run positioning, content, and launches for Series A-B SaaS companies.

If you need the whole system, consider working with me.

Nobody on the team opens it. Designers eyeball colors from the live site. Writers guess at tone. Every piece of content becomes a mini-negotiation: “Is this too casual? Should we use ‘we’ or the company name? Do we capitalize that?” And when you bring in a new agency, freelancer, or AI tool, you hand them that dusty PDF and hope for the best.

The problem is that brand guidelines were designed for humans who read documents. Your marketing stack in 2026 includes AI writing tools, vibe coding sessions, and content workflows that run on structured data — not vibes. A PDF with “our primary color is blue” doesn’t help Claude Code build a landing page that actually looks like your brand. A paragraph about “conversational tone” doesn’t help an AI produce copy that sounds like your founder.

What you need are two things: a visual identity system (how your brand looks) and an editorial voice system (how your brand sounds) — both structured enough that humans and machines can use them consistently. That’s what the brand-kit and tov-guidelines skills produce.

How it works — visual identity extraction

The brand-kit skill takes a screenshot-first approach. You feed it 3-5 screenshots of the company’s website — homepage, about page, pricing page, a feature page — and Claude’s vision analyzes the actual pixels.

Why screenshots instead of scraping CSS? Because what you *see* is the source of truth. CSS gets compiled, overridden, conditionally loaded. A Framer site injects styles dynamically. A Tailwind build compiles utility classes away. But a screenshot of the live homepage shows you exactly what a visitor sees — the real colors, the actual spacing, the true typography.

The skill extracts across eight sections:

• Section 0: Visual identity description — a natural-language design brief written for vibe coding reproduction. Not “modern and clean” but “dark velvet backgrounds with strategic spotlights picking out what matters — the visual equivalent of a founder who wears a quality black t-shirt instead of a suit.” Specific enough that an AI tool can recreate the aesthetic from the description alone.

• Section 1: Colors — primary palette (8-15 colors) with hex values, text colors, color ramp, dark/light mode rules. Every color gets a confidence score from 0-5.

• Section 2: Typography — typefaces with fallback stacks, type scale (at least 5 sizes), letter spacing, CSS quick reference.

• Section 3: Components — primary button, secondary button, cards, navigation — each with CSS code blocks you can copy directly.

• Section 4: Data visualization — chart color ramp, container styles, label fonts. Because dashboards and reports need brand consistency too.

• Section 5: Logo usage — variants, rules, file paths.

• Section 6: Spacing — base unit, scale, container widths. The rhythm that makes a design feel intentional.

• Section 7: Signature elements — the 3-5 visual details that make this brand instantly recognizable. Gradient rays behind hero text. A scrolling logo marquee. Tab-based service navigation. These are the elements a designer copies first and a copycat misses entirely.

The confidence layer

Every extracted token gets a confidence score:

The skill never invents a hex value. If it can’t extract a color with confidence, it marks it [NEEDS VERIFICATION] with its best approximation. Same for fonts — if the typeface can’t be identified, the skill describes the letterform shape and suggests likely candidates, clearly flagged.

When CSS extraction is available as a supplement (user provides the URL alongside screenshots), the skill cross-references both sources. Where they agree, confidence jumps to 5. Where they disagree, the screenshot wins — because the screenshot is what the visitor actually sees.

How it works — visual identity extraction

The tov-guidelines skill takes a two-phase approach. Phase 1 extracts evidence. Phase 2 generates guidelines. A human review gate sits between them — because voice patterns need validation before they become rules.

Phase 1: Analysis

The skill scrapes 15-20 pages from the company’s website, prioritized by voice signal density:

1. Homepage and about/manifesto (core positioning, personal voice)

2. Blog posts (3-5) for long-form voice

3. Case studies (2-3) for proof presentation style

4. Pricing and services for commercial tone

5. FAQ for conversational tone

Then it extracts patterns across four levels:

Sentence-level — average sentence length, first-person vs. third-person usage, question frequency, imperative usage. Not “they write short sentences” but “12 words average, first-person ‘I’ on 5 of 6 pages (83%), 2-3 questions per page.”

Paragraph-level — average length, opening patterns, transition patterns, evidence placement.

Word-level — recurring company vocabulary, customer vocabulary, banned/avoided words, modifier frequency.

Structural — header style (sentence case vs. title case), CTA placement and phrasing, proof stacking patterns, section organization.

Every finding gets a frequency score:

• High (80%+) — pattern appears across most page types

• Medium (40-79%) — pattern appears on some pages

• Low (<40%) — pattern appears rarely

• Conflict — contradictory patterns found

Conflicts are flagged, not hidden. If the homepage uses “I” but the pricing page uses “we,” the analysis says so. If blog posts are 200-word casual reads but case studies are 1,500-word formal reports, that inconsistency gets documented with source URLs for each.

Phase 2: Generation

After the user reviews Phase 1 findings — correcting any misidentified patterns, resolving inconsistencies, answering gap questions — the skill generates the final guidelines document.

The document answers six critical questions:

1. Who’s actually reading? — primary reader definition with job title, challenges, goals

2. What does tone sound like? — before/after examples, not adjectives

3. What patterns repeat vs. vary? — pattern library with LLM-specific guidance

4. What words actually used? — vocabulary with company terms, customer terms, banned words

5. What structure fits? — opening patterns, body structure, CTA style

6. What to refuse? — anti-patterns with specific phrases to avoid

That last question is where the skill gets genuinely useful for AI-assisted content. Every TOV document includes an AI-speak detection section — the structural patterns that instantly read as machine-generated:

Every guideline traces back to a source URL from Phase 1. No invented examples. No floating claims. If someone questions why the guidelines say “use first-person ‘I’” — the source says it appeared on 5 of 6 pages at 83% frequency, with the URLs listed.

See it in action

Company: Linear (linear.app) — the issue tracker that engineers actually want to use.

Input: Website URL only. No upstream research required.

Extraction: Homepage, case studies, pricing, integrations, G2 reviews (196 reviews, 4.5/5), solutions pages, and partner references.

The skill identified 22 High-confidence data points, 14 Medium, and 5 Low — a confidence score of 4 out of 5.

Visual extraction (brand-kit)

Three screenshots analyzed: homepage, features page, changelog.

Visual identity description excerpt:

Dark, focused, and premium. The design feels like a mission control center for serious builders — purposeful without being cold.

Visual metaphor: “A high-end audio mixer in a professional recording studio at night — every element precisely placed, dark surfaces that recede, and purple accent lights that guide your attention.”

Deep black backgrounds create infinite depth while the signature purple (#5E6AD2) marks every action point — buttons, links, and progress indicators. The purple doesn’t overwhelm; it guides. Gray text (#8A8F98) stays readable but recessive, creating clear hierarchy.

Extracted tokens (excerpt):

• Signature elements: Keyboard-first UI animations. The gradient mesh backgrounds. Status-colored issue labels. Minimal chrome with maximum information density.

Prompt for reproduction:

“Create a dark-themed project management interface with mission control aesthetics. Use pure black (#000000) backgrounds. Primary accent is muted purple (#5E6AD2) — never loud, always guiding. Cards on slightly lighter surfaces (#111111). Typography is tight, geometric, with aggressive tracking. Every pixel serves function. The design should feel fast even standing still.”

That description — plus the full token inventory — is enough for a vibe coding session to produce a landing page that *feels* like Linear without copying any specific element.

Voice extraction (tov-guidelines)

15 pages analyzed: homepage, about, blog (5 posts), changelog (3 entries), docs landing, careers, customers, pricing.

Key findings (Phase 1 excerpt):

Voice-in-action example:

• Anti-patterns identified: No superlatives (”best,” “leading,” “revolutionary”). No excitement language (”thrilled,” “excited,” “game-changing”). No feature bragging — features are stated, not sold. Writing that sounds like an engineer wrote it because an engineer *did* write it.

• Output: A complete TOV guidelines document with sentence patterns, vocabulary lists, structural templates, and an AI-speak filter — all sourced to specific URLs, all frequency-scored.

When to use it

• First week of a new client engagement — run both skills before positioning, messaging, or content strategy. The brand-kit feeds landing page wireframes, mockups, and vibe coding. The TOV guidelines feed every content skill. Without these, every downstream output starts from scratch.

• When multiple people create content for the same brand — two writers, an agency, a freelancer, and Claude Code all producing “on-brand” work without a shared system means five different interpretations of the brand. The structured outputs from both skills become the single source of truth everyone references.

• After a rebrand, product expansion, or founder transition — when the visual identity or voice has evolved but the documentation hasn’t caught up. Run brand-kit to capture the new visual reality. Run tov-guidelines to extract the current voice patterns. Update the system, and every downstream tool inherits the changes automatically.

Get the Skill

Both skills are open source on GitHub:

• Brand Kit — visual identity extraction (8 sections, confidence scoring, screenshot-first)

• TOV Guidelines — editorial voice extraction (two-phase, 6 questions, frequency scoring)

Save each SKILL.md to your `.claude/skills/` folder, then run `/brand-kit` or `/tov-guidelines` in Claude Code. The skills handle inputs, source chains, and quality gates automatically.

These are two of 90+ GTM skills I’ve built in Claude Code to run positioning, content, and launches for Series A-C SaaS companies. The brand system they produce feeds landing pages, LinkedIn content, sales materials, and every other deliverable — once extracted, it compounds across everything.

If you need the whole system — or someone to run it for your brand — let’s talk.

You’ve got the persona doc. One page, maybe two. A cartoon avatar named “Marketing Mary” with a job title, three pain points, and a quote you wrote yourself because you couldn’t find a real one. The sales team has never opened it. The content team doesn’t reference it. And every time someone asks “who’s our ICP?” in a meeting, three people give three different answers.

The problem isn’t that you haven’t done ICP work. It’s that most ICP work produces a static document built on assumptions — not a structured research output built on verified data from your actual customers.

When ICP research is systematic, everything downstream sharpens. Positioning lands because it’s grounded in real buyer language. Outreach converts because pain points come from G2 reviews, not brainstorms. Content resonates because you’re writing to people whose jobs, challenges, and buying triggers you’ve actually documented — with sources.

This is the skill I run at the start of every client engagement. It produces a 12-section ICP report with TAM analysis, firmographic segments, champion and economic buyer deep-dives, negative ICP criteria, and intent signals — all sourced, all confidence-rated. And now it’s systematized in Claude Code.

How it works — step by step

The skill runs in three phases. Phase 1 extracts everything the company’s website and third-party sources already tell you. Phase 2 synthesizes that raw data into structured segments, personas, and signals. Phase 3 assembles the final 12-section report with confidence levels on every claim.

Phase 1: Extract everything the website already tells you

Before analyzing anything, the skill fetches at minimum eight pages from the target company’s website: `/customers`, `/case-studies`, `/solutions`, `/use-cases`, `/pricing`, `/integrations`, `/industries`, and the homepage.

Each page serves a specific purpose. The customers page gives you named logos — the foundation for firmographic analysis. Case studies are the richest source: they contain job titles (personas), quantified outcomes (proof points), and verbatim quotes (voice of customer). The pricing page reveals value metrics, tier logic, and size indicators. The integrations page maps the adjacent tech stack.

Then it searches externally. G2 reviews are where buyers describe problems in their own language — not the polished version marketing invented. Capterra adds volume. Reddit threads surface segments the company doesn’t publicly claim. LinkedIn company pages and job postings reveal hiring signals and growth patterns.

For each data point extracted, the skill records three things: the data itself, the source URL, and the access date. No floating claims. If the source is a case study, you get the URL. If it’s a G2 review, you get the URL. If the data doesn’t exist, the skill writes “Not available” — never an invention.

After extraction, all attributes get normalized. Geography maps to standard regions (US, EMEA, APAC, LATAM, Global). Industries map to a fixed vertical list. Company sizes normalize to revenue bands and employee counts. This normalization is what makes cross-company and cross-segment analysis possible.

Phase 2: Analyze across 12 dimensions

This is where the skill separates from a typical persona exercise. Instead of one dimension (buyer profile), it researches twelve.

• TAM analysis. The skill calculates total addressable market using either bottom-up (customer count x average contract value) or top-down (market size x addressable share) methodology. But the key output isn’t the TAM number — it’s the ICP row below SOM. That’s the highest-priority segment based on everything the research surfaces. Each layer (TAM, SAM, SOM, ICP) gets a targeting strategy, so the numbers aren’t just academic — they tell you where to allocate effort.

• Firmographics with segment deep-dives. Geography, industry, and company size tables — each with evidence URLs and confidence levels. But the skill goes deeper than tables. For each company segment (Enterprise, Mid-market, SMB), it produces a structured deep-dive: priorities within today’s market trends, ICP-fit rationale, budget and sales cycle expectations, unique targeting approach, and proof points with named customer outcomes. These deep-dives are what make the research actionable for sales and marketing — not just descriptive.

• Technographics. Required vs preferred tools by category: CRM, analytics, data warehouse, cloud, marketing automation, e-commerce. This tells you which technology prerequisites indicate fit and which tools appear alongside the product. If you’re selling to companies that use Snowflake and Segment, that’s a technographic signal worth filtering on.

• Champion and Economic Buyer deep-dives. This is the core persona work — and it’s significantly deeper than a standard persona slide.

  • The Champion deep-dive has 12 explicit fields: titles, department and team size, responsibilities, jobs to be done, success metrics, challenges, pain points, current alternatives, problems with alternatives, buying behavior, channels and influences, and a verbatim testimonial. Channels and influences is where it gets specific — not just “they read blogs” but which LinkedIn groups, which Slack communities, which podcasts, which events, which influencers they follow.

  • The Economic Buyer deep-dive has 13 fields. It adds three that Champions don’t get: “what they actually care about” (the real decision drivers beyond stated concerns), “common objections” (typical pushback during sales), and “what a no-brainer looks like” (the conditions that make budget approval easy). These three fields are worth the entire research project for a sales team.

• Negative ICP. Identifying who is NOT a fit — with disqualification criteria, red flags from churned customers, and objection patterns that signal poor fit versus legitimate concerns. Most ICP work skips this entirely. The negative ICP is what stops your sales team from wasting cycles on deals that were never going to close.

• Intent signals. Company-level events (new executive hire, funding announcement, tech stack change, M&A activity) and persona-level behaviors (role change, LinkedIn posts about relevant challenges, competitor content engagement). Each signal gets a detection source — where to actually find it — so the output feeds directly into prospecting workflows.

• Voice of customer synthesis. Terminology patterns, pain point language, outcome language, and objection patterns — all verbatim, all sourced. This section is what makes your messaging authentic. When your landing page uses the same language your buyers use in G2 reviews, conversion improves because the copy matches how they think about the problem.

Phase 3: The anti-hallucination layer

Every claim in the final report gets one of three confidence levels:

• High — Direct from official source, verifiable (case study, pricing page, press release)

• Medium — Third-party source, multiple signals (G2 review, news article, community mentions)

• Low — Single indirect source, inferred (forum mention, job posting, analyst estimate)

The report opens with a confidence score (1-5) calculated from the ratio of High to Low data points. If you’ve got 70%+ High confidence data, you’re at a 5. If it’s mostly Low, you’re at a 1.

Section 11 (Data Gaps) is mandatory. It documents every piece of missing information: what was searched, why it matters, and how to fill the gap. “Not available — searched G2, Capterra, and company blog; no case studies found” is infinitely more useful than an invented data point.

Section 12 (Source Appendix) lists every URL accessed, with dates and what was extracted. This means anyone reviewing the research can verify any claim by clicking the source link.

The tools running in the background — and what they cost

The ICP research skill uses web scraping and search tools to extract data from company websites, review sites, and third-party sources. Three MCP servers handle the heavy lifting.

Apify — the primary scraping engine

Apify is a marketplace of 3,000+ pre-built web scrapers called “Actors.” The skill uses Apify to scrape case study pages, G2 reviews, customer logo sections, and pricing pages — anything where structured data extraction is faster than manual reading.

For ICP research specifically, Apify handles:

• Deep site crawling via the RAG Web Browser Actor (fetches and parses entire sections of a site)

• G2 and Capterra review extraction (structured JSON with reviewer titles, company sizes, quotes)

• LinkedIn company data scraping (employee counts, job postings, growth signals)

Setup (5 minutes): Create an account at apify.com → Settings → API Token → add as remote MCP server in Claude Code.

Cost: Free tier gives $5/month. A full ICP research run — scraping 8-10 pages, pulling G2 reviews, extracting LinkedIn data — runs about 6-8 Actor calls. The free tier handles one full research run per week comfortably.

Firecrawl — browser rendering for JavaScript-heavy pages

Some pages (especially interactive pricing calculators, dynamic customer showcases, and review sites) are JavaScript-rendered — direct HTML fetches return nothing. Firecrawl spins up a headless browser to render and extract these pages.

Setup (5 minutes): Create an account at firecrawl.dev → copy API key → add as remote MCP server.

Cost: Free tier gives 500 credits. One page scrape = 1 credit. A typical ICP research run uses 30-60 credits depending on how many JavaScript-heavy pages need rendering.

Exa — semantic search for finding what Google misses

Exa is a search engine built for AI workflows. Where Google returns pages, Exa returns structured answers. For ICP research, it surfaces niche community discussions, analyst reports, and comparison articles that standard searches miss.

Setup (2 minutes): Create an account at exa.ai → add as remote MCP server.

Cost: Free tier gives 1,000 searches/month. An ICP research run typically uses 10-20 searches.

All three services connect as remote MCP servers. Claude Code calls them automatically when the skill needs them. You don’t manage API calls or parse HTML — the skill handles the tool chain and fallback logic.

See it in action

Company: Strapi (strapi.io) — the leading open-source headless CMS

Input: Website URL only. No upstream research required.

Extraction: Homepage, case studies, pricing, integrations, G2 reviews (196 reviews, 4.5/5), solutions pages, and partner references.

The skill identified 22 High-confidence data points, 14 Medium, and 5 Low — a confidence score of 4 out of 5.

What the research surfaced

TAM calculation (Section 2):

Champion deep-dive excerpt (Section 4):

Champion: Frontend Developer / Tech Lead. Titles include Full-Stack Developer, Engineering Team Lead, Backend Developer. Sits in Engineering, team of 3-15 developers. Measured on shipping velocity, code quality, and system reliability.

Pain points surfaced from G2 reviews: “WordPress is slow and inflexible.” “Manual code updates for content changes.” “Plugin compatibility issues.” Buying triggers: new project kickoff, WordPress migration, need for headless architecture.

Verbatim VOC: *”Honestly this is by far the easiest solution for jumping into the headless CMS world”* — G2 Review. *”Strapi save me so much time... easy to add tables and relations between them”* — G2 Review.

Negative ICP (Section 5):

Non-technical small businesses (no developer on team — Strapi requires technical implementation). WordPress loyalists expecting themes and plugins (different paradigm). Teams wanting all-in-one SaaS including frontend (headless = bring your own frontend). Companies with budget under $10K/year total (route to Community Edition only).

ICP segments with positioning recommendations (Section 8):

1. High-growth tech companies (35K companies, $20K ACV) — “The API-first CMS developers love. Ship faster, scale effortlessly, own your content infrastructure.”

2. Enterprise digital transformation (6K enterprises, $150K ACV) — “Consolidate your content infrastructure. One headless CMS for all channels, regions, and teams.”

3. Media and publishing (9K companies, $50K ACV) — “Content at the speed of news. Publish anywhere, update instantly, scale to millions of articles.”

4. Digital agencies (17.5K agencies, $20K ACV) — “Ship client projects faster. One CMS your team masters, every client benefits from.”

Output: Full 12-section report with segment prioritization, per-segment messaging recommendations, content strategy by segment, and outreach recommendations by channel. Ready to feed directly into positioning, messaging, and outbound workflows.

When to use it

• First week of a new client engagement — run this before positioning, messaging, or content strategy. It establishes the customer foundation everything else builds on.

• When your sales team targets everyone — the segment scoring matrix and negative ICP give sales a clear filter for who to pursue and who to disqualify, with evidence behind every criteria.

• When positioning feels generic — if your homepage could belong to three different companies, the ICP research surfaces the specific language, pain points, and proof points that make positioning concrete instead of abstract.

Get the Skill

Want the full 12-section methodology, dimension schemas, search patterns, and output templates?

Get the skill on GitHub

This is one of 50+ GTM skills I’ve built in Claude Code to run positioning, content, and launches for Series A-B SaaS companies. If you need the whole system, consider working with me.

Most competitive research ends at the homepage. You skim the features page, check if they have a pricing page (they don’t), maybe Google their name and skim a G2 review or two. Then you write a positioning doc based on vibes.

The problem isn’t effort — it’s method. Without a systematic framework, you miss the signals that actually matter: how they’re acquiring customers, what their reviews reveal about product gaps, whether their job postings signal a pivot, what ads they’re running at the bottom of the funnel.

Structured competitive intelligence changes what you can build on top of it. When positioning, messaging, battlecards, and sales decks are grounded in verified competitor data — not assumptions — every downstream deliverable gets sharper.

This is the skill I run before any positioning engagement. And now it’s systematized in Claude Code.

And that’s what I’ll be sharing every week — one Claude skill every week for free to help you 3X your output and velocity as a GTM operator, and get one step closer to owning your outcomes.

How it works — step by step

The skill runs in four phases. The core is Phase 2, where you research 13 dimensions systematically. Each dimension has a prescribed set of sources, a fallback chain if primary sources fail, and explicit confidence levels on every finding.

Phase 1: Confirm before you research

Before anything, lock down the competitor URL. This sounds obvious until you realize “Bolt” could mean ride-sharing, fintech, or Bolt.new (StackBlitz’s AI coding product). The skill prompts yo to verify the exact company, confirm the website URL, and choose your research mode — single deep dive (all 13 dimensions) or comparison matrix (3-6 competitors, core dimensions only, tabular output).

Phase 2: Research all 13 dimensions

This is where the real work happens. Each dimension has a primary source chain and a fallback sequence:

• Dimension 1 — Company: Crunchbase and LinkedIn for funding, headcount, HQ. You’re looking for signals like runway, growth rate, burn posture.

• Dimensions 2-3 — Product + ICP: Homepage, features page, docs, and case studies. The ICP work goes one layer deeper — searching Reddit for who actually uses the product surfaces segments the company doesn’t publicly claim.

• Dimension 4 — Pricing: The skill has a five-step fallback chain. Start with their `/pricing` page. If it’s hidden or “contact us”, check G2 snippets, then Vendr (which publishes negotiated contract data), then Reddit threads for user-reported pricing. Only after exhausting all four sources do you mark pricing as unavailable.

• Dimension 5 — Reviews: G2, Capterra, and Reddit for unfiltered sentiment. The reviews dimension surfaces the product gaps and support frustrations that competitors won’t mention in their own marketing — and that become your positioning ammunition.

• Dimensions 6-7 — Content + Launches: Blog, resource library, event pages, and Product Hunt. You’re extracting topic themes, format mix, cadence, and any recent announcements in the past 90 days.

• Dimension 8 — SEO/AEO: If Ahrefs MCP is available, pull domain rating, top organic keywords, and referring domains directly. If not, fall back to Serper.dev for SERP position checks on 5-10 category keywords. You want to know where they rank, what they rank for, and whether they’re showing up in AI-generated answers.

• Dimensions 9-10 — Technographics + Openings: Integrations page for tech stack signals; careers page and LinkedIn Jobs for hiring patterns. A sudden cluster of SDR/BDR postings is a GTM signal. A slate of enterprise CS roles signals a segment shift.

• Dimension 11 — GTM: This one requires reading between the lines. Analyze CTAs, check job descriptions for mentions of outbound sequencing tools (Outreach, Salesloft, Apollo), and search Reddit for buyer-reported experiences of their sales process. PLG or sales-led? Inbound-heavy or outbound-aggressive? This shapes how you position against them in deals.

• Dimension 12 — LinkedIn/Social: Company page follower count and growth, post frequency and content types, founder activity. The question: are they building an organic content moat or is their brand presence thin?

• Dimension 13 — Paid advertising: All three ad libraries (Google Ads Transparency Center, LinkedIn Ads Library, Meta Ads Library) are JavaScript-rendered — direct HTML fetches return nothing. The skill uses two APIs running in the background to get around this:

  • Apify is the primary method. It’s a marketplace of cloud-hosted scraping “Actors” — serverless apps that someone else has already built and maintained for scraping specific sites. The skill auto-discovers the right Actor for each ad library, checks its input schema, then calls it with the competitor’s domain. Three tool calls in sequence: search-actors (find the right scraper) → fetch-actor-details (check what inputs it needs) → call-actor (run it). You get structured data back: active campaign count, ad copy themes, CTA patterns, geographic targeting.

  • Firecrawl is the fallback. When no suitable Apify Actor exists for a particular ad library, Firecrawl spins up a headless browser session. It navigates to the ad library URL, renders the JavaScript, and extracts what’s on the page. Two tool calls: browser_create (launch the session) → browser_execute (interact with the page and extract data).

  • If both fail — which is rare — the skill logs it as a data gap with a manual check URL. But here’s the thing: no active ads found is itself a high-confidence data point. It tells you the competitor is sales-led or organic-first, not that the research failed.

The APIs running in the background — and what they cost

Dimension 13 is the one that trips people up when they try to replicate this manually. The ad libraries are JavaScript walls — you can’t just fetch the HTML. But with two MCP servers connected to Claude Code, the skill handles it automatically. Here’s how to set them up.

Apify — the primary scraping engine

Apify is a marketplace of 3,000+ pre-built web scrapers called “Actors.” You don’t write scraping code — you find an Actor someone already built (e.g., “Google Ads Transparency Scraper”), pass it the competitor domain, and get structured JSON back.

For the competitor research skill, Apify handles:
→ Google Ads Transparency Center scraping (highest priority for B2B SaaS)
→ LinkedIn Ads Library scraping
→ Meta Ads Library scraping
→ G2/Capterra review scraping (Dimension 5)
→ Deep site crawling via the RAG Web Browser Actor

Setup (5 minutes):

1. Create an account at apify.com

2. Go to Settings → Integrations → API Token → copy it

3. In Claude Code, ask it to add Apify as a remote MCP server

4. That’s it — the skill auto-discovers the right Actors at runtime via search-actors

Cost: Free tier gives $5/month in platform credits. Lightweight scraping (like ad library checks) costs fractions of a cent per run. A full competitor research pass — 3-4 competitors, 3 ad libraries each — runs about 12 Actor calls. The free tier handles that comfortably.

Firecrawl — browser rendering fallback + site crawling

Firecrawl is an API for crawling and scraping websites with full browser rendering. Where Apify uses pre-built Actors, Firecrawl gives you a raw headless browser that can navigate and interact with any page — including JavaScript-rendered ones that return empty HTML on direct fetch.

For the competitor research skill, Firecrawl handles:
→ Fallback for Dimension 13 when no Apify Actor exists for a specific ad library
→ Full competitor website crawling for deeper product and content analysis
→ Any JS-heavy page that needs browser rendering

Setup (5 minutes):

1. Create an account at firecrawl.dev

2. Copy your API key from the dashboard

3. In Claude Code, ask to add Firecrawl as a remote MCP server

Cost: Free tier gives 500 credits. A single page scrape costs 1 credit. A full competitor site crawl uses 50-200 credits depending on site size. For a typical 3-competitor research run, budget 200-600 credits. The Hobby plan ($16/month) gives 3,000 credits if you need more.

I’ve been running this skill across 6 active client engagements per month. I’ve never needed to go beyond the free tiers.

Both services connect as remote MCP servers — Claude Code talks to them automatically when the skill needs them. You don’t manage API calls or write scraping code. The skill handles the tool chain, the fallback logic, and the error handling.

Phase 3: Synthesis

After all 13 dimensions, you assign confidence levels to every claim (High / Medium / Low), write a 2-3 paragraph executive summary, and document data gaps with explicit follow-up actions. The Iron Law here: no data point without a source.

“Widely known” doesn’t count. Estimates need their reasoning shown. “Not available” is always better than invented data.

Phase 4: Aggregate analysis (multi-competitor)

Once you’ve run two or more deep dives for the same client, Phase 4 kicks in. You cross-reference all the individual profiles and ask Claude Code to run the aggregate insights of all competitors. You get:

• A threat matrix ranking competitors: PRIMARY / ENTERPRISE TIER / DIRECT ICP / STEALTH WATCH / LOW / DEFUNCT — each justified with evidence, not gut feel

• A feature parity matrix classifying capabilities as Commoditized / Differentiator / Emerging

• A credibility signal audit comparing funding, press, enterprise logos, and founder visibility

• Strategic recommendations tied to exploitable vulnerabilities — each with rationale, evidence, and effort/impact scoring

The aggregate output is one core ingredient that feeds positioning. Individual deep dives give you facts. The aggregate gives you strategy.

See it in action

Company: Ramp (corporate spend management)

Scenario: Ramp’s PMM team needs competitive intelligence on Brex before refreshing their positioning.

Competitor inputs:

• Competitor: Brex

• Website: brex.com

• Client context (optional): Ramp competes head-to-head in mid-market; Brex recently shifted enterprise

Phase 2 excerpt — what the research surfaces:

• Pricing (Dimension 4): Brex’s pricing page shows a free tier and an “Enterprise” tier at “Contact us.” G2 snippets reveal negotiated contracts typically $15-25/user/month for mid-market. Vendr data confirms 12-month contracts with 15-20% negotiation room. Confidence: Medium.

• Reviews (Dimension 5): G2 shows 4.7/5 (1,200+ reviews). Reddit surfaces a recurring complaint: “Brex’s support response time degraded significantly after their pivot to enterprise.” This becomes a positioning wedge — Ramp’s mid-market responsiveness vs. Brex’s enterprise-first resource allocation.

• Openings (Dimension 10): Brex LinkedIn Jobs shows 8 open Enterprise AE roles and 3 Enterprise CSM roles, zero SMB-focused positions. Confirms the upmarket pivot is structural, not messaging-level.

• Paid (Dimension 13): Apify scrapes the Google Ads Transparency Center and returns 47 active search campaigns. Primary copy themes: “scale your business spend” and “enterprise finance platform.” No mid-market language in any creative — they’ve written out of that segment entirely.

Output: Executive summary with 22 verified data points, 9 inferred, 4 estimated. Data gaps flagged: Brex revenue estimate unavailable (Sacra and PitchBook checked, no public figure). Four follow-up actions suggested. Ready to feed directly into Ramp’s positioning refresh.

When to use it

• Before a positioning sprint — run this on 3-4 competitors first. before writing a single positioning statement

• When a sales rep keeps losing deals to the same competitor — the GTM and reviews dimensions can surface why

• When a new competitor enters your market — single deep dive, fe mins, and get the full picture before anyone on your team starts panicking

Get the Skill

Want the full 13-dimension methodology, source chains, and output templates?

Get the Skill on GitHub

This is one of 50+ GTM skills I’ve built in Claude Code to run positioning, content, and launches for Series A-B SaaS companies. If you need the whole system, consider working with me.

I’m Matteo Tittarelli, founder & solopreneur at Genesys Growth, a B2B SaaS product marketing and content consulting.

I’m a solo operator, which usually means my capacity is the ceiling. With AI, I try to stretch that ceiling to the max, allowing me to scale my output without scaling headcount.

In 2025, that leverage allowed me to handle up to 6 clients per month on my own, while keeping a (mostly) normal workweek without turning his calendar into a sleep-deprivation experiment.

Today, The Workflow scrutinizes one of my wildest and most successful AI experiments so far. Scaling competitor and category comparison pages (1640+, to be exact), killing manual work while keeping the quality bar high.

Impossible? Let’s see.

PS. Subscribe to The Workflow if you haven’t already.

✅ What you’ll learn

• How to scale 1600+ competitor pages without sacrificing quality

• How to scale SEO content without producing AI slop

• How to use Clay for programmatic SEO

🧨 What triggered The Workflow

Comparison pages became a recurring client request for Matteo. SaaS in crowded markets with thousands of products and features need content that explains why they’re different (and better). There are so many ways to approach this: comparing tools, feature categories. Then adjacent categories. Before long, you’re staring at hundreds of pages.

Doing this manually wasn’t an option if he wanted to keep his sanity. Pricing changes. Reviews age. Feature claims drift. At scale, errors spread fast.

So the GTM engineer re-engineered the process.

He built a workflow that pulls together live market data, reviews, and pricing, anchored by a structured positioning library. The result is a scalable comparison-page engine that stays fresh, doesn’t become a maintenance nightmare, and drove a 38% increase in organic impressions just two weeks after the first pages went live.

🛠️ How to build The Workflow

To build this workflow, start by defining what matters in your ICP’s buying decision. Only then use AI to handle the grunt work needed to scale. You don’t just want to create pages, you want to express how you see the market.

Here’s how the pieces connect, from the positioning that drives every comparison to a scalable hub of product marketing pages.

Steal Matteo’s prompts and save yourself the trial and error.

Step 1: Build a structured messaging and competitor library

Ahhhhh, the good old foundational work required to do anything interesting with AI.

Use Octave to centralize the building blocks of a company’s narrative. Core messaging, sales decks, customer intelligence, and selected competitor data live in one structured place.

Each product is broken down into the same core blocks: description, differentiated value, capabilities, problems solved, and customer proof. That structure also applies to personas, use cases, segments, reference customers, and competitors.

“You’d be surprised how many companies don’t have a real source of truth for their messaging.”

Matteo, we believe you.

Congrats! You just created a reusable positioning library that feeds comparison pages, sales enablement, and outbound personalization, without breaking consistency.

Step 2: Messaging fine-tuning

The last thing you want is to scale wrong or inconsistent messaging.

Use Octave’s natural language chat interface to review what was ingested, correct inaccuracies, resolve contradictions, and clean up inconsistencies across products, personas, and competitors in your library.

Step 3: Wireframing

While refining the messaging, it’s critical to think ahead about how that information will live on the pages. This step intentionally overlaps with messaging work to prevent bottlenecks later.

Matteo recommends collecting examples of ideal landing pages, then building section-by-section wireframes for each page type in Figma. Make sure to follow a clear hierarchy:

• a central competitor hub that acts like a homepage,

• reusable templates for product vs competitor, product vs category, and competitor vs competitor pages.

Each template should follow the same core structure, from navigation and hero to comparison tables and supporting sections.

Every wireframe is designed with future automation in mind. Each section corresponds to a specific data input that will later be generated and populated programmatically. Locking the structure and hierarchy at this stage allows the system to scale to hundreds of interlinked pages without collapsing later.

Step 4: Build the programmatic comparison powerhouse in Clay

With the wireframes locked, move into production. Clay is the workspace where every comparison page is assembled at scale, using the messaging and positioning already defined in Octave.

Instead of creating pages one by one, Matteo creates tables. Each table represents one type of page. One table powers product vs competitor pages. Another powers product vs category pages. A third handles competitor vs competitor pages for any specific category (and you can scale this for as many adjacent categories you have!). The layout of each table is almost fixed (only a few comparison fields can change, but it’s up to you). What changes from row to row is the page’s subject, such as a specific competitor, category, or pairing.

Think of each row as one future page, and each column as one piece of that page. That’s the foundation everything else builds on.

First come the input columns. These define what each row represents, such as a competitor, category, or URL. This is the minimum information needed to anchor a page.

Next are the research columns. Clay scrapes competitor websites, pulls reviews from sources like G2 and Capterra, and fetches logos via Brandfetch.

Then come the copywriting columns. This is where Octave comes back in. Use the Octave content agent inside Clay to pull messaging and positioning from the library created in step one. That context is combined with the research data to generate SEO metadata, headlines, comparison tables, pricing sections, differentiation paragraphs, and FAQs.

Because each column maps to a specific section of the page, Clay is able to fill predefined slots.

Tip: Work in small batches to protect quality and avoid burning credits (or connect your Anthropic API for saving Clay credits altogether!). Test prompts on single rows, refine them, and run the full table only then. Once it holds, the setup can be reused across categories with minimal changes.

Grab Matteo’s best prompts to populate your Clay tables

Step 5: Push pages live to your Content Management System

Time to bring these gems live! Matteo loves Webflow as a CMS because it offers precise field mapping and solid SEO controls.

Each output column in Clay maps to a specific Webflow CMS field. Headlines, subheadlines, logos, comparison tables, pricing sections, FAQs, and SEO metadata all have predefined destinations, which makes publishing smooth as butter.

Pages can be pushed either to staging or directly live, but one rule holds: never publish too many at once. Start with a small batch, review it carefully, fix copy or layout glitches, and only then scale up to publishing hundreds of pages.

The real win is maintenance. Because pages are generated from Clay tables, updates don’t require manual rework. Dynamic fields like pricing or reviews can be rerun and pushed to Webflow without turning updates into a nightmare.

🤖 Tools powering the Workflow

This isn’t an easy workflow, but the logic applies to any project that needs to scale. AI and automation only amplify the foundation you give them. Here, positioning comes first and speed follows. And yes, Matteo more or less invented a whole new use case for Clay 🤯

🎢 Highs, lows and Workflow warnings

✅ What shines

• Crazy results, fast
  It took about 1.5 months to launch the first 500 pages. But two weeks after launch, organic impressions were up ~40%, clicks rose ~150%, and CTR doubled (on an already three-digits baseline ;). Fresh pages also started surfacing regularly in AI overviews for queries like “[competitor] pricing”.

• Set up once, reuse forever
  The real strength of this workflow is its versatility. The same structure supports every type of comparison page, so expanding into new categories like “[your brand] vs [category]”, “[competitor] vs [category]”, or “[category] vs [category]”, or adding new players, becomes almost plug-and-play.

❌ What doesn’t shine

• Quality assurance is a pain
  Even when everything looks perfect in Clay, issues tend to pop up once content hits the CMS. Fields don’t always stitch together cleanly (especially with custom HTML code blocks), mappings break, and small glitches appear. Expect manual fixes and ongoing QA maintenance, especially at scale!

• Slow time to value
  The upfront work is heavy. Wireframing, design, prompt tuning, tone calibration, CMS field mapping, and custom HTML code and workflow setup all take time. Matteo spent roughly two months getting the first large batch live. After that, the ROI compounds.

⚠️ Workflow warning

This workflow only makes sense in hypercompetitive, mature markets with complex, feature-rich products. To make it work, you need to know how to build strong landing pages and understand both buyer intent and LLM scraping behaviour. AI and automation aren’t the only hard part here.

✨ The Goldflow

While everyone and their mother is obsessing over scaling faster, Matteo is obsessing over getting the fundamentals right.

“AI needs to be told what good looks like. That only works if you have context: your ICP, your market, your competitors.”

Yet another reminder that scale and tools aren’t strategy. They never were. They never will be.

Sara Stella & Diandra ✌🏼

🏗️ Send this to the friend who still builds comparison pages one by one.

Share

• Why most founders struggle to explain what their product actually does

• The common mistakes on homepages

• Why AI makes everything sound the same

• And how to make your message simple and clear

If you’re building 0→1, this will help you fix your messaging fast.

The age of AI is no longer emerging — it is dominantly. Upon. Us.

As of late 2025, a staggering 88% of organizations mentioned they use AI in at least one business function. In marketing and sales, this has led to a widespread tactical use of LLMs for everything from summarizing meetings with Granola to generating ad copy with Claude, to full-blown workflows of intent-based outbound across Clay and Smartlead, or AI content generation with n8n, AirOps, and Webflow.

But here’s the paradox nobody is talking about. While everyone is using AI — or trying to — it’s very few who are really succeeding with it.

We’re in a state of “prompt anarchy” — a tactical free-for-all grab of prompt templates, libraries, and masterclasses (even we have some!) free that’s seemingly creating value, but can also be so overwhelming that is creating some analysis paralysis.

This AI and prompting craze is the reason why we’re witnessing a massive “GenAI Divide”. On one hand, they say it’s spreading brain rot making content marketers and operators lazier. On the other hand, it’s skewing the real GTM alpha on the teams with the larger, most structured, data sets — and the ambition to mine them.

The proof is in the numbers. A recent MIT report found that a shocking 95% of generative AI pilots at enterprise companies are failing to make it to production. This represents a massive waste of resources and a catastrophic failure to capture AI value.

This is compounded by two other critical business risks:

• The infrastructure gap: While 77% of executives feel urgent pressure to adopt AI, only 25% believe their IT infrastructure is actually ready for enterprise-wide scaling. It’s like trying to run a Tesla on a lawnmower engine. This shows not just that we’re still early, but that to look at real innovation in AI usage, we’re better off turning to the smaller, more nimble players that are building these AI workflows software — that’s where the true AI alpha lies. More on this below.

• The prompt management gap: Alex Halliday, founder of AirOps, mentioned: “Your prompts are your IP — keep them optimized and manageable like an engineer would”. But instead, most GTM teams treat prompts like disposable scripts instead of strategic assets. This leads to inconsistent quality and zero reusability — a far cry from the industry benchmark of 40-60% prompt reuse that high-performing teams achieve.

So, in an environment where 95% of initiatives fail, how can GTM leaders cross the GenAI Divide and join the top 10% mining the real GTM Alpha? How do you transform scattered, failing pilots into a cohesive, strategic, and scalable growth engine that drives measurable business results?

There’s not a one-size-fits-all solution. BUT, we believe it starts with managing your prompts better — like they were actual IP to nurture, polish, and constantly refine.

And doing so in a system fashion — with what we call a Prompt-Led Engine (PLE).

This requires treating prompts with the same rigor as other critical GTM assets like ad campaigns or sales playbooks. It’s what we see top operators doing at the GTM Engineer School, where students from the likes of Ahrefs, Google, Slack, and more learn how to build automated systems across AI GTM like Clay, n8n, Cargo, Octave, and AirOps to automate 10-hour manual processes and reduce manual effort by 80%.

In this post, we’ll walk you through our systematic approach to designing, testing, managing, and deploying prompts as core components of your automated GTM strategy.

And we’ll give you examples of who’s doing it very well, so you know who to look up to for what good looks like!

Let’s dive in.

1. The 100x value multiplier of a Prompt-Led Engine

The journey from prompt anarchy to a Prompt-Led Engine isn’t just about getting organized — it’s about unlocking exponential value.

It’s a strategic shift from treating prompts as disposable commands to architecting them as durable, scalable assets.

As Brendan Short of The Signal notes, the future of GTM tech is about selling automated labor, not just software”. According to Brendan,

“The GTM company that will break out beyond a $1B valuation (and into $100B territory) will be the one that builds something that looks like labor in the form of swiping a credit card (instead of sourcing+ramping+managing people, equipped with software).”

However, as we’ve learned in our careers, you can’t “boil the ocean in one pot” — you have to think in phases, in maturity levels like Crawl, Walk, Run. This is how you build that future, one level of maturity at a time.

And here’s how you can apply this thinking to your prompt-led engine.

Level 1: The Individual Prompt

An operator uses a one-off prompt to trigger a task in a chat UI like ChatGPT or Claude. Nothing new here.

Example: A marketer uses a prompt to write a single blog post. The value is limited to that one asset. This is where 95% of AI pilots die, trapped in a cycle of one-off efforts.

You might use a specific prompt generator to structure your prompt but the prompt is not saved, shared, or optimized. It’s a dead end, offering a fleeting moment of productivity but no lasting value.

Level 2: Shared, Manual Prompt Library

Want to know all the details of the other levels,  plus real-life applications of this framework from real companies? Head over to read the full article.

Read the full article on GTM Strategist

1. BUILD A MESSAGING & POSITIONING LIBRARY IN Octave

I do this first because this library forms the context engine for every campaign, landing page, and assets I create. It has:

↳ Product messaging: capabilities, benefits, differentiators
↳ ICP, segments, and use cases model
↳ Competitors, customer references, proof points

The best part is that you can build it for free in <1h (with the right inputs).

Octave is great not just for starting out with messaging/positioning, but also for:
↳ Keeping things consistent across outbound/content channels
↳ Adding messaging when introducing new products/segments/personas
↳ Re-positioning or re-messaging against new competitors/categories

2. EXPORT YOUR OCTAVE LIBRARY AS LLM KNOWLEDGE

I export Octave library as a Google Doc and feed it to AIs.

↳ GPT/Claude/Manus AI/n8n: Context for long-form/enablement assets
↳ AirOps → Knowledge base for SEO/content (optimization) workflows
↳ Scripe → Create GOOD LinkedIn ideas/posts with ~one-shot
↳ Clay → Create landing pages at scale (also call content agents directly)

This way content is always grounded in deep ICP, product, competitors knowledge — and writing styles (I have a custom GPT to generate those).

3. EXPAND YOUR LANDING PAGE FOOTPRINT

Must haves these days:
↳ Product/use case pages to explain what you do (in details)
↳ Persona/Solutions pages to call out your buyer personas/segments
↳ Comparison pages to compare you with alternative/status quo
↳ Integration pages to show how you fit into your buyers’ stack

SaaS hardly have one competitors/personas/use cases these days, that’s why you need to some extent most if not all of the above!

These work as PMM/positioning assets, but also for AI/SEO coverage.
And you can scale these very easily and WELL with Octave + LLMs.

With one client, I’ve recently created 500+ competitors pages with Octave + Clay + Webflow. It took about ~2.5 months of work.

4. STACK MULTIPLE SOCIAL PROOF MARKERS

Social proof has never been more table stakes, and required across multiple formats and placements.

You MUST collect LOTS of logos, testimonial, reviews, proof points, and case studies. Don’t wait on it. And SHOW it ALL — preferably above the fold.

↳ Above the fold logo + case study + testimonial on hover
↳ Testimonial + proof stats nested in product sections
↳ G2/SOC2 badges + stars + scale markers (eg. trusted by 50k+ orgs)

Clay does this super well on their website (see below).
I die a little bit every time I see logos below the fold.

5. START POSTING ON SUBSTACK + LINKEDIN YESTERDAY

Own 1 category-related word (these days better with “AI” as a prefix), and start sharing your POV about it. Your real experience, your own thoughts, your own point of view. Just share how you see things.

My fave people doing this are Austin Hughes, Brendan Short (thx for inspiring this post ;), Emir Atli (Hiring AEs and SDRs), Stan Rymkiewicz, Linda Lian, Kevin Indig, Kyle Poyar, David Fallarme, Maja Voje.

And you HAVE TO do long-form too — piggy-back off writers in your niche with large audiences. If you’re not on the map, people won’t buy from you!

6. USE VISUAL IDENTITY/DESIGN AS A BRAND DIFFERENTIATORS

Your looks matter A LOT more than what you think on your perception of trust and end up.

Best examples for me are Clay, Linear, Anthropic.
↳ Clay infuses actual clay-ness into their iconography
↳ Anthropic stands out among “robotic players” with their humane doodles
↳ Linear proudly owns their hyper-minimalist, hyper-refined dark mode

Invest time to THINK about your ICP, your ALTERNATIVE, and how you can come across as DIFFERENT not just in your product/copy, but also in your look/feel. It’s a more blue ocean than you think.

Want to build your product marketing foundations? I’ve helped 40+ Seed-to-Series C build their messaging, website presence, and content. Book a call with me to collaborate.

Book a call with Matteo

7. KEEP A TASTE SWIPE FILE OF CREATORS, BRANDS, COMPETITORS

Make a simple list — competitors, accounts, websites, campaigns, creators — that defines GOOD looks like for you (and your ICP).

Use it as training data for LLMs, and run comparative analysis to identify gaps + opportunities for your campaigns (Manus AI is great for this).

8. CONNECT + COMMENT WITH YOUR ICP DAILY

Connect manually with up to 100 ICP connections per week (200 with premium). That’s ~500 new connections per month, and the more you connect the more the algo gives you ICP-like connection suggestions.

Everyone says this, but no one does it — comment on posts from your target buyers. It doesn’t just give you extra reach, but it keeps you current, makes your name top-of-mind beyond just posting.

BUT DON’T do AI here 😡

Have a follow up question? Chat with my Digital Mind on Delphi.

Chat to my Digital Mind

I’ve watched teams spend months crafting beautiful positioning decks. Detailed competitive analyses. Elegant brand frameworks that would make McKinsey proud (but not make any $$$).

Then nothing moves.

The strategy is sound, but the tactical playbook reads like academic theory. Email sequences that sound like white papers. Sales decks that explain the market but don’t close deals. Content that impresses peers but doesn’t convert prospects.

Strategy without execution becomes theatre. Impressive to watch, but it doesn’t drive pipeline.

The execution hamster wheel

The opposite extreme is equally deadly. Teams sprint on cold outreach campaigns, paid ad experiments, and nurture flow optimizations. Activity becomes the metric. Motion feels like progress.

But without clear strategic direction, every tactic feels diluted. Your outreach sounds like everyone else’s because you haven’t defined what makes you different. Your ads generate clicks but not qualified pipeline because you’re targeting symptoms, not root causes.

Execution without strategy becomes a hamster wheel. Lots of energy, no forward movement.

What balance actually looks like

The teams that break through don’t swing between extremes. They balance both sides of the GTM equation simultaneously:

• Positioning and channels. Your differentiated story must match where your audience actually discovers solutions. A contrarian positioning strategy that lives only in sales decks is worthless if your prospects find alternatives through SEO.

• Narrative and nurture. Your brand story should flow seamlessly into your email sequences, sales conversations, and product onboarding. Every touchpoint should reinforce the same differentiated thesis.

• Strategy and execution. Your tactical choices should ladder up to strategic positioning. Your strategic decisions should inform which tactics get resources and which get cut.

Not over-indexed one way or the other. Not divorced from reality.

Need help finding the right mix in your GTM? Join companies like Ahrefs, Common Room, Fiverr, Strapi, FullEnrich, Toast, and many more who I’ve already worked with.

Book a call with me

The feedback loop that changes everything

Here’s what most teams miss: it’s not a one-way flow from strategy to execution.

Every channel you run feeds insights back into your strategy. SEO reveals which problems prospects actually search for. Paid ads show which messaging resonates at scale. Sales calls expose the real objections your positioning needs to address.

That feedback loop is where GTM excellence lives. Strategy informs execution, execution refines strategy, and the entire system gets sharper over time.

Teams that master this balance don’t just avoid the extremes. They create a virtuous cycle where every tactical win makes their strategy more precise, and every strategic insight makes their tactics more effective.

• The tactical test: Does your current execution clearly reflect your strategic differentiation? If someone experienced your touchpoints blind, would they understand what makes you different?

• The strategic test: Do your positioning decisions directly inform which tactics get resources and which get cut? Can you draw clear lines from your core thesis to your channel investments?

Most teams fail one test or the other. The winners pass both.

A 5-week live cohort for GTM operators who want to master scalable, AI-driven workflows that deliver measurable ROI.

• From messaging to sequence/content: Each week = 1 complete workflow system.

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• Featuring expert instructors from top GTM teams + breakouts for hands-on support.

• $7000 worth of tool credits included + prompt libraries + certification.

ROI projection: 375-848% based on 5-10 hours saved weekly.

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And now on today’s post!

Most B2B SaaS teams get trapped in a false choice: broad reach or sharp focus. But the real choice is simpler and more important than you think.

Your messaging strategy sits on two critical axes that determine whether you generate pipeline or just noise.

The messaging matrix that reveals the trap

Every SaaS company falls into one of four messaging quadrants based on how they balance audience focus and point of view:

• TAM pleaser (Broad + Generic) Safe story trying to appeal to everyone. You sound exactly like your competitors because you're afraid to take a stance. Result? Lots of website visits, zero pipeline velocity. If you’re in this quadrant, ask yourself: “Why are you even playing the game?”

• Just noise (Narrow + Generic) You've identified your ICP but your story is bland within that niche. "We help Series B fintech companies optimize processes." You can't stand out from competitors who target the same slice with equally safe messaging.

• Empty sensation (Broad + Differentiated) You have a contrarian take that gets attention, but you're speaking to everyone and no one. Lots of LinkedIn followers, podcast downloads, and "thought leadership" credits. Pipeline stays flat because nobody feels specifically called out.

• Sharp and impactful (Narrow + Differentiated) The sweet spot. You combine laser focus on one ICP with a differentiated story that challenges how they think. This generates actual pipeline because the right people immediately understand why they need you now. Now, I know what you think, but it takes guts, creativity, and focus, but it can be done.

Why the sweet spot works

When you nail narrow focus plus contrarian POV, three things happen that directly impact your pipeline:

• Ready-to-buy prospects self-select in. Your messaging acts like a filter. Time-wasters bounce off, but qualified prospects think "finally, someone who gets our specific problem."

• Deal velocity accelerates. Sales conversations start with context instead of education. Prospects already understand your differentiated approach before the first call.

• Competitive differentiation becomes automatic. You're not fighting feature wars or price wars because your positioning is fundamentally different from alternatives.

The hard truth about positioning

Most founders avoid the sweet spot because it feels risky. Narrow focus means saying no to potential customers. Contrarian POV means some people will disagree with your approach.

But here's what's actually risky: trying to please everyone while standing for nothing.

The TAM pleaser approach feels safe but kills conversion. The empty sensation approach gets ego validation but not revenue validation.

Your messaging audit

Ask yourself these two questions:

1. Focus test: If I removed my company name and logo from my website, would my ICP immediately know this message is for them specifically?

2. Differentiation test: Does my core message challenge how my market thinks about the problem I solve, or does it sound like everyone else?

If you can't answer yes to both, you're not in the sweet spot yet.

Where does your current messaging land on this matrix? Book a call with me to strategize which quadrant you want to be in.

Book a call with Matteo

Making the shift

Moving to sharp and impactful messaging requires three decisions:

1. Choose your ICP ruthlessly. Not "mid-market companies" but "Series B fintech startups struggling with compliance automation after their first regulatory audit."

2. Develop a contrarian thesis. Challenge conventional wisdom and current alternatives about how your problem should be solved. What does everyone else get wrong?

3. Have the courage to polarize. Some prospects will disagree with your approach. You’ll upset some incumbents. That's the point. You want the ones who resonate strongly, not the ones who sort of agree.

The sweet spot isn't comfortable. It's the only profitable way to stand out in the sea of sameness of the AI era.

Disclaimer: this is a sponsored product deep dive by Clarify, the autonomous CRM for founders. Their team paid me to write this long-form newsletter. As always though, I’ll remain as unbiased as possible in this deep dive.

Introduction

Most early-stage B2B founders — whether building alone or with a co-founder — find themselves covering the entire go-to-market operation. Even with a co-founder, the duo often ends up juggling everything: sales calls, writing follow-up emails, creating content, supporting customers, and steering the product roadmap. This used to mean choosing between scaling the business or keeping founders deeply involved in every critical touchpoint.

However, there's a challenge that's becoming impossible to ignore: hiring early reps doesn't solve the core problem. Or bringing on a VP of Marketing or Head of Sales often creates more overhead — both financial and operational — than actual leverage because you’re still writing the playbook. These hires are best suited for when you have a bit of a signal on what’s working in your early GTM motion, sales process, etc. so they can accelerate.

Meanwhile, founders who try to do everything manually hit an inevitable ceiling — their personal capacity becomes the bottleneck that limits company growth.

So how do you scale founder influence and expertise without diluting what makes founder-led GTM so effective in the first place — the context, authority, grit, and speed that only comes from the person who’s building the product?

The breakthrough lies in a fundamental reframe: successful founders in 2025 aren't just great at executing GTM tactics — they're exceptional at designing AI-powered systems that multiply their unique advantages.

Instead of hiring their way out of hands-on involvement, they're building lightweight, intelligent systems that handle the repetitive work while preserving the high-impact moments that only they can deliver.

In this post, I’ll argue what these systems look like and how you — early-stage founder of a VC-backed startup — can implement them.

1. The founder credibility that no hire can replicate

First things first, let’s discuss why founder-led GTM is still critical.

Early-stage founders convert prospects at rates that would make junior sales hire jealous, and there's a simple reason why: they possess an irreplaceable combination of market context, depth of product knowledge, authority, and decision-making speed.

When a founder says "we built this feature specifically because customers like you told us X," that carries weight no sales rep can match. When a prospect asks about roadmap priorities or technical capabilities, founders provide answers with confidence and nuance that builds trust instantly.

But here's the trap most founders fall into: they assume this advantage only exists in 1:1 interactions. They schedule endless demos, write personalized emails one by one, and turn every customer conversation into a marathon call. The founder's influence remains trapped in individual moments — and an impossible to manage calendar — instead of scaling across the entire GTM motion.

The insight that separates thriving founder-led companies from overwhelmed ones is understanding that founder’s calendar is a zero-sum game — both early-stage founders and Elon Musk have all 24 hours in a day.

Once you work back from this insight, you realize that founder credibility can be expanded and scaled — systematized. The goal isn't to remove the founder from the process — it's to design systems that amplify your voice, capabilities, and output across multiple channels and touchpoints, from initial outreach to customer success.

So how do you scale that influence without burning out?

2. The mindset shift: From top seller to GTM system designer

The most successful founders have made a crucial mental shift: they've stopped thinking of themselves as the person who does all the work and started thinking like the architect who designs compounding systems, workflows, and loops to scale their work.

Here are some founder-led GTM examples I follow that substantiate the above:

• Austin Hughes, co-founder and CEO of Unify, provides a blueprint for founder-led GTM driven by transparency, social content, and rigorous systems.

  • Hughes credits intentional team-building and daily public content creation as core drivers in scaling Unify to 56 employees and a major Series B in just 2.5 years.

  • “Pick a social channel and work at it every day. The teams that understand viral content today have an unfair distribution advantage. As a founder, its so important to figure out how to nail one. For us that's Linkedin.” he wrote in August 2025, while explaining that distribution is the new bottleneck, not product.

  • Using LinkedIn as a compounding distribution engine, Austin posts tactical insights and playbooks, making Unify’s GTM motion both transparent and repeatable for other founders.

  • This founder-as-systems-architect approach is evident in his willingness to document what worked (and what didn’t), inviting the ecosystem along for the journey and setting the tone for next-gen founder-led SaaS.

• Adam Robinson, CEO of RB2B and Retention.com, exemplifies founder-led GTM at scale by turning LinkedIn into a compounding engine for both revenue and growth and product feedback.

  • Within two years of consistent content, Robinson’s posts have driven 140k+ followers and made RB2B’s LinkedIn presence its core distribution channel. He’s frank about the power of founder narrative, writing, “Building a personal brand on LinkedIn is the fastest, most efficient, and highest leverage way to grow a startup that has ever existed.”

  • By sharing granular journey updates — product launches, lessons, frameworks, and failures — Robinson has built not just a company but a movement, driving $5M in ARR with a five-person team in just one year.

• Amos Bar-Joseph, CEO of Swan AI, demonstrates the new frontier of founder-led GTM by using content and AI to scale revenue per employee to unprecedented levels.

  • With only three founders and zero outbound or paid ads, Swan AI generates $1.20 in pipeline for every LinkedIn impression, reaching $30M+ ARR after just a few months on the market.

  • Bar-Joseph’s LinkedIn content acts as a top-of-funnel rallying cry: each manifesto-style post reframes how founders should think about intelligent-scale and operational simplicity. “We replace scaling challenges with intelligence, not bodies,” he explains, outlining how AI agents absorb system drudgery so humans can focus on high-impact decisions.

  • This systematic, high-leverage approach — articulated in dozens of detailed posts — positions Amos as both a teacher and chief architect, showing how founder-led GTM is no longer about hustle, but compounding trust and efficiency.

• Anton Osika, CEO and co-founder of Lovable, offers a deeply personal account of founder-led go-to-market in his July 2025 LinkedIn post announcing Lovable’s $200M round.

  • He writes: “I’ve never shared how it started: I called my friend at 6AM and told him I wanted to build the world’s first AI platform that anyone could use. I did the first 20 sales calls myself. I personally wrote, called, or DM’d our first 100 beta users to learn what was broken and what they loved. Being on the front lines gave me the conviction and clarity to move harder and faster — those early GTM loops became the playbook we scaled with the team.”

  • This firsthand narrative highlights how Anton himself executed the core founder-led GTM motions — personally driving early sales, running product feedback loops, and architecting the motion from direct customer interaction. He frames these activities as the foundation for Lovable’s explosive, system-driven growth.

• Amanda Zhu, founder of Recall, shows how founder-as-educator content can turn complex, technical products into movements customers rally behind.

  • Instead of relying solely on sales outreach, Amanda uses her LinkedIn presence as a live journal of building Recall, weaving product storytelling with candid lessons in founder resilience. Her posts range from detailed breakdowns of AI workflows to transparent reflections on the emotional toll of scaling a startup — building Recall’s narrative as much around authenticity as technology.

  • Where others might treat social updates as marketing collateral, Amanda’s voice serves as Recall’s differentiator. She translates raw conversations with researchers, operators, and early adopters into digestible thought leadership that doubles as customer enablement.

  • Amanda’s approach — turning what she’s learning in real time into frameworks, metaphors, and stories that resonate with both buyers and peers — illustrates the next stage of founder-led GTM. Her content isn’t detached branding; it’s an open-source field guide, where every post compounds Recall’s credibility, community, and demand.

• Gal Aga, CEO of Aligned, demonstrates how system-led content drives outsized pipeline for founder-led SaaS.

  • Gal built a repeatable, metrics-driven workflow that elevated LinkedIn into Aligned’s top channel for pipeline creation, with his leadership content frequently generating 1.5M+ impressions a week.

  • He doesn’t just recount personal wins; his LinkedIn feed is a steady stream of “hard lessons learned” and sales frameworks, systematizing experience into influential, teachable moments. In June 2025, his post summed up the ethos: “I led sales teams between $1M-$100M ARR. Built 4 orgs from scratch. Made every mistake in the book...” Gal’s approach — turning individual expertise into content and feedback loops that reliably bring in new customers — is the blueprint for founder-led GTM that scales beyond the founder’s calendar.

Each of these founders shows that the modern founder-led GTM playbook is about designing compounding, system-driven workflows — content, product, and outreach loops that operate at scale — rather than purely individual effort. Their systems create an outsized surface area of influence, trust, and revenue, all rooted in authentic founder context and voice.

• Instead of manually researching prospects, they build research workflows.

• Instead of writing individual follow-up emails, they create intelligent sequences.

• Instead of hoping valuable sales conversations turn into content, they build systems that automatically extract insights and generate marketing assets.

This system-designing approach tackles three core areas that typically consume founder bandwidth:

1. Outbound prospecting, outreach, and pipeline management.

2. Product feedback, support, and roadmapping.

3. Content creation that scales their reach, authority, and brand awareness.

Each system is designed with the same principle — handle the repetitive, time-intensive work automatically while preserving space for founder involvement in the high-leverage moments that drive real business outcomes.

The founders who master this approach often share a similar realization story: they spend one week building a system that saves them 10+ hours every subsequent week, and they immediately see the compound effect. What used to require constant founder attention now runs with minimal oversight, freeing them to focus on product development, strategic partnerships, and the customer conversations that actually move the business forward.

In the GTM Engineering School I run, VC-backed founders like Andrea from Aptus learned how to design these systems hands-on — from scaling prospecting and research to personalized outreach and automated content creation.

This is the mindset shift — with the right attitude, tooling, and a bit of work, founders can become the foundational GTM Engineer of the company, balancing deep business understanding, endless grit and commitment, with creativity and scalable processes.

3. The three-system framework with tools that compound

The most effective founder-led GTM systems operate across three interconnected areas, each designed to multiply founder impact rather than replace founder involvement.

Picture this scenario:

• A founder used to spend 40+ hours per week on GTM activities — manually researching prospects, writing personalized emails, transcribing sales calls, and trying to remember which customer said what about which feature.

• Six months later, that same founder spends 12 hours per week on GTM while generating 3x more qualified pipeline.

The difference? They built what we call the "founder multiplication engine" — three interconnected systems that transform individual effort into scalable competitive advantage.

The founders who crack this code start with a carefully chosen set of interconnected tools. But here's the twist — tools are powerful, but it's the system design between them that drives exponential results.

System 1: Outbound that runs without you

The architecture begins with an outbound motion that operates like an intelligent researcher.

• Smart founders use tools like Octave for defining, storing, and updating ICP, messaging, and positioning in real-time.

• Then, using these strong PMM foundations, they call Octave agents to enrich, qualify, and write sequences directly into Clay, which is used for TAM sourcing and orchestration — so every touch is hyper-tailored and sent only to ICP-fit opps.

• Then, messages are piped into Smartlead, Instantly, or other cross-channel tools across LinkedIn, email, phone calls.

• Last but not least, this compounding loop is closed with Clarify as the autonomous CRM that centralizes all interactions and learns from every call — automating follow-ups, meeting prep, and pipeline management.

This first system can scale the founder-led sales multiple folds by automating prospect research, outreach personalization, and follow-up — but here's the crucial element: AI manages the process until the founders step in for the highest-leverage conversations. The system learns from every interaction, constantly improving personalization and timing.

System 2: Customer conversations become product features

The second system turns every customer interaction into compound product prioritization and value.

Instead of letting valuable sales conversations disappear into memory, systematic founders use AI-native CRMs like Clarify to automatically extract patterns from every customer call, identifying common objections, successful positioning approaches, and emerging use cases.

These insights feed directly into product feedback, feature prioritization, and roadmapping. Tools like Linear can ingest structured feedback from Clarify to inform lean product and engineering organizations as to how to build the right features faster — thanks to AI code editors like Cursor, Copilot, or Windsurf.

Lean GTM-savvy founders that truly value speed and shipping will also use tools like Lovable to vibe code mini-products, websites, or feature upgrades with 10X time-to-market and a fraction of the costs.

This is another important mindset shift that is happening with AI — from documenting feedback to directly building and shipping based on feedback-based prompts.

System 3: Content that is grounded in your ICP and scales

The third system creates content that compounds over time rather than requiring constant creation.

The most efficient founders have discovered something counterintuitive: their best product marketing insights around positioning, messaging, GTM, and content already exist in internal documentation, sales call recordings, and customer conversations.

• They use conversation intelligence from CRMs like Clarify to extract patterns from sales calls, identify common objections and success stories, and automatically generate a backlog of product marketing and content themes.

• Insights from these calls can be fed back into Octave in plain English to update ICP models, product messaging, comparative analyses with competitors, customer proof points, and more. These insights will trickle down immediately into your sequence agents and re-align your outbound messaging accordingly.

• With this new system, a single customer conversation — recorded as a podcast episode — can also become the foundation for a case study, sales enablement materials, a LinkedIn video clips post series, a blog article, and even be featured in thought leadership internal research for sales enablement purposes.

This reveals another fundamental mindset shift — create once, use a thousand times — across multiple placements.

• Riverside and Opus.pro are great, cost-effective tools for podcast creation and clip distribution — including automatic cross-channel bulk scheduling on social, while tools like AirOps or Scripe are great to turn call transcripts into long- and short-form text-based content that sound just like the founder.

• These tools all enable human-in-the-loop reviews to maintain voice and accuracy while letting AI handle the heavy lifting of adaptation and distribution.

• And for the crazier lead magnets, mini-website, or full-fledged digital products — depending how ambitious you are — there is also Lovable.

• This content is crucial for founders to build organic audiences and momentum on must-have platforms like LinkedIn and Substack. The key is tracking which content drives qualified traffic and pipeline, not just impressions, creating a feedback loop that improves content quality over time.

Each tool has a specific role in these systems, but the strategic advantage comes from how they connect together — creating workflows where customer insights automatically improve outreach and product marketing messaging, where successful conversations become marketing assets, and founder authority scales across every touchpoint without diluting its authentic impact or adding overheads.

This is the mechanism that lets founders scale their authority without adding more overheads — one authentic customer conversation becomes the foundation for weeks of assets.

Want to build your founder-led GTM engine? I’ve helped 40+ SaaS founders build their system-led stack and motions. Book a call with me to collaborate.

Book a call with Matteo

4. The modern founder-led GTM stack is made of tools that connect and compound

But here's where the compounding effect really emerges: these systems don't operate in isolation — they form an intelligent ecosystem where each component makes every other component more effective.

But every system needs a central node and source of truth to coordinate the most important data — customer relationships. And in B2B SaaS, such a central node has always been and still is the CRM.

AI-native CRMs like Clarify serve founder-led GTMs as autonomous command centers, learning from every customer interaction and feeding insights back into each systems we outlined above — outbound, product, and content.

When your prospecting system learns from your sales conversations, when your content creation pulls from actual customer language, and when your follow-up sequences adapt based on conversation intelligence, you've moved beyond tool collection into true system design.

The power multiplier comes from this system integration — when your outreach tool learns from your sales conversations, when your content creation system pulls themes from actual customer language, and when your follow-up sequences adapt based on conversation analysis.

This is where conversation intelligence becomes the command center of the entire system.

Instead of manually updating spreadsheets or trying to remember important details from last week's sales calls, the system captures and processes every conversation, turning raw interactions into actionable intelligence.

5. Scaling founder-led GTM without losing authenticity

The biggest fear most founders express about systematizing GTM is losing what makes their personal involvement so valuable. They worry that automation will make their outreach feel robotic, that AI-generated content won't capture their voice, or that intelligent follow-up will miss the nuances that build real relationships.

The reality is that well-designed systems — with thought-through foundational context from the founder — can amplify authenticity rather than diminish it.

• When AI handles prospect research, founders can spend more time on personalized insights that actually matter.

• When conversation intelligence extracts patterns from customer calls, founders can speak more precisely in their customers' language.

• When content systems generate drafts from founders' actual words and experiences, the final output becomes more authentic, not less.

The key is designing systems that preserve founder involvement in the moments that matter most while eliminating the busywork that dilutes their impact.

Start with 80% founder involvement and 20% system support, then gradually shift to 20% founder involvement in high-leverage moments and 80% system handling everything else. This transition allows founders to maintain their authentic voice and relationship-building advantage while dramatically increasing their reach and efficiency.

The founders who nail this transition often describe a similar outcome: they're having more meaningful customer conversations, creating better content, and driving more qualified pipeline — all while working fewer hours on GTM activities. Their systems handle the repetitive work, but their personal touch shows up exactly where it creates the most value.

Closing thoughts

The most successful startups in 2025 won't be the ones with the biggest marketing budgets or the fastest hiring timelines. They'll be the companies where founders learned to design systems that multiply their unique advantages — their product context, market authority, and decision-making speed — without burning out in the process.

This systematic approach to founder-led GTM creates a compound advantage that traditional hiring can't match. Every customer conversation improves the system's intelligence, every piece of content strengthens the founder's market authority, and every successful outreach sequence becomes a template for scaling further.

The future belongs to founders who don't just sell — they design. They turn customer conversations into intelligence, insights into systems, and personal influence into scalable competitive advantages.

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And now, on today’s post!

Brilliant founders often bury their message in jargon.
While average ones sometimes get most traction.

That doesn’t seem fair.
Both are smart. And both have strong products.

So why does one message resonate while the other sinks?

It’s not clever copy.
And it’s not squeezing in every feature.

The real issues are more structural.

→ Messaging that’s too diluted to appeal everyone.
→ Overloading on "agentic" feature names instead of capabilities.
→ GTM functions telling three different stories across different channels.

And those are just a few.

After consulting 40+ startups and speaking with 100+, I distilled my top 10 list of messaging mistakes across three categories.

It’s all packed into one graphic of the 10 most common pitfalls I see kill GTM traction.

You’ll see patterns that keep products from breaking through, no matter how good they are.

The positioning pitfalls that kill conversion

Here are the top 10 product marketing mistakes B2B SaaS founders make, based on the infographic:

1. Diluted messaging

• Trying to be everything to everyone creates vague, forgettable positioning.

• Sharp focus beats broad appeal every time.

2. Features over capabilities

• Using fancy internal product names instead of describing what it actually does.

• Buyers care about outcomes, not your engineering terminology.

3. Ignoring "why now?"

• Forgetting to embed urgency that connects to your ICP's specific use case.

• Timing creates the burning need to act.

4. Lack of contrarian opinion

• Avoiding bold differentiation out of fear of "upsetting" incumbents or partners.

• Safe positioning is invisible positioning.

5. Cross-team misalignment

• Sales, marketing, and product all telling different stories creates narrative debt.

• One story, told consistently, builds trust.

The research and strategy gaps

6. Conviction over customer POV

• Wrong balance between founder gut feelings and actual customer insights.

• Your opinion matters less than your buyer's reality.

7. No competitive intelligence

• Ignoring how rivals position themselves makes differentiation impossible.

• You can't stand out if you don't know the landscape.

8. Static messaging

• Not refreshing positioning as markets shift and competitors evolve.

• Yesterday's winning message becomes today's background noise.

The execution errors

9. Overly technical language

• Ego-boosting jargon alienates buyers who want clarity above cleverness.

• Simple wins over sophisticated.

10. Treating positioning as tactical

• Postponing messaging work instead of treating it as upstream business strategy.

• Positioning drives everything else in your GTM motion.

TL;DR?

→ The hardest part of messaging isn’t copywriting.
→ It’s treating messaging like upstream business strategy.

The fix? Start with customer research, build contrarian points of view, and keep messaging sharp and customer-focused.

If Common Room, Toast, AirOps, Ethena, Fiverr, Ahrefs trust me with their product marketing, you can too. I’ve helped 40+ SaaS companies nail their message — book a call to make yours just as strong.

Book a call with Matteo

(I know this should be obvious, but so often isn’t).

PRODUCT MARKETING.

→ Start with product marketing and you’re rock solid.

Without a strong product marketing foundation…

→ Content becomes fast food quality.
→ Messaging is watered down.
→ AI makes the noise louder.

The only way to build a GTM system that compounds is to
Start with Product Marketing at the core:

That means:

→ ICP — Know your customers, intimately
→ Positioning — How you’re better than alternatives
→ Messaging — Articulate, not sell, your value prop

That solid core gives you the clarity to create content that resonates, because you know EXACTLY whose taste you’re designing for.

Here’s how I think about the three layers of GTM:

Core: Product Marketing
What you do (better) and who’s it for

→ ICP model
→ Comparison pages
→ Competitor analysis
→ Messaging and positioning
→ Case studies and proof points
→ Sales calls and win/loss analysis
→ Home, persona, use cases, solution pages

Middle: Content Marketing
How you articulate your Product Marketing

→ Thought leadership, ebooks
→ Blog content and newsletter
→ Video explainers, webinars
→ Email nurture sequences
→ Podcasts & whitepapers

Outer: Growth Marketing / Distribution
Where you inject your messaging and content

→ Community seeding — Slack, Discord, Reddit
→ Affiliates, influencers, and strategic referrals
→ Technical and programmatic SEO
→ Partnerships and co-marketing
→ Paid search and paid social
→ Cold and warm outbound
→ Event sponsorships, PR

When the core is ultra clear, the middle layer can perform
Because it’s targeted and differentiated.

And once content is working, the outer layer has something worth scaling.

TLDR:

→ Invest in the foundations of product marketing.
→ Build all content within product marketing.
→ Distribution will work itself out.

This is where I help clients: getting the core right
so the second layer can actually work.

A client recently ended our engagement because my pace was out of sync with the rest of the org capacity.

And in hindsight, it made sense.

↳ If you flood the pipeline faster than design, ops, or sales can absorb, you don’t get more growth, you create bottlenecks.

You can have the best strategy and the cleanest execution, and still fail because you pushed a system past its limit.

↳ Speed without capacity in mind is just chaos in motion, not velocity.

Which led me to think…

Every company has their own invisible “absorption rate, the pace at which it can take in new work, new change, new opportunity.

• If you match it, the whole system goes smooth.

• If you exceed it, the system doesn’t accelerate.

Because speed isn’t a universal advantage.

↳ It’s a conditional advantage.

It works only when the rest of the org can move in sync.

__

Which raises bigger questions for any team:

→ Are you moving at the right speed for your system?

↳ Or are you chasing “fast” without checking if the rest of the machine can keep up?

→ How do you increase the absorption rate without creating lag-time?

↳ How do you push growth without triggering friction?

These are the things no one puts in a playbook.

But they’re often the difference between the 0.1% who make it and the 99.9% who just try.

It definitely made difference in my operator experience.

And I’ll use this hard earned lesson with my future clients:

- Find their absorbption rate early

- And match it to align with the whole ship

Have you ever seen speed backfire?

__

I’m collecting anonymous “untold startup stories”.

The messy, uncomfortable truths we skip in case studies and testimonials.

Because if we don’t talk about them, we can’t fix them.

PS. DM me if you want to share your story

(I’ll keep it anonymous).

Let’s make the startup world a little more real.

• What’s truly broken in SaaS GTM

• Why is the explosion of AI content making things worse

• Why does clear messaging beat volume today

• Founders’ biggest differentiation mistakes

• Why does founder visibility matter more than ever

• How can startups use AI without falling into the sameness trap

Disclaimer: this is a sponsored product deep dive by Taplio — their team paid me to write this long-form newsletter. However, I’ve used Taplio and it has helped me over and over streamlining my content research and production operations. Also, I’ll remain as unbiased as possible in this deep dive, sharing my product feedback to make Taplio an even better product for solopreneur marketers like me!

PS. Read until the end to get an exclusive promo I’ve worked out with the Taplio team just for you!

Introduction

LinkedIn has become a noisy, look-alike feed, and cracking the code on standout content is tougher than ever. When every post feels like a remix of the last, even figuring out what “good” looks like can feel like hunting for a hay in a haystack, especially if you’re just doom-scrolling.

And if you’re a one-person consultant like me who has to actually write LinkedIn content for a living, things can get even harder.

Here’s what that a day in the life of a solo LinkedIn content ghostwriter means in practice:

• Run account and content research across different industry verticals

• Stay on top of the viral formats, accounts, and trends for each vertical and ICP

• Ghostwrite in multiple founder voices without losing their nuances or writing styles

• Prove content’s impact on audience, leads, and pipeline — not just vanity metrics

• Do all of the above for your own consulting business

• And craft and share your own opinions as a marketing operator, showcasing case studies, testimonials, learnings, and wins to attract your next customers

Keeping that many plates spinning begs the question — how do you balance:

• Trendiness: Staying aligned and up to date with “the trend” and formats that go viral

• Differentiation: While carving a standout, “different”, and unique POV

• Volume: And shipping consistently to stay top of mind?

I’ve found Taplio to be of great help to meet the right balance — blending research, AI writing assistance, and light-touch project management in a single UI.

In this newsletter, I’ll walk you through my end-to-end LinkedIn workflow and show exactly how Taplio can help streamline it. I’ll also include some tips for the Taplio team on where I’d love to see the product go next. 🙂

The LinkedIn content lifecycle

In B2B SaaS, LinkedIn only compounds when every step of the content loop feeds the next — here’s the full circuit I run, end-to-end, for both my clients and myself:

1. Research: I start by abstracting specific topics mapped to ICP pain points and use cases, scanning viral formats for algorithm-friendly hooks, and pinpointing the tastemakers and trendsetters accounts who shape the vertical’s opinions and trends.

2. Strategy: blend those insights together to carve a unique point of view and a series of content pillars — and related sub-topics — aligned with your value prop and what your ICP cares about.

3. Writing: turn your strategy topics into actual posts packaged with scroll-stopping hooks, easy-to-skim structure and formats; that sound unmistakably “you.”

4. Collaboration & feedback: share your draft with collaborators, designers, or your clients for feedback until the message is crisp, the tone of voice ticks, the creative is matching, and you’re ready to ship.

5. Distribution & scheduling: slot each post into a visible calendar so you (and your clients) know what’s up next and how it ties into the rest of the calendar for the week(s).

6. Engagement & comments: jump into comment threads where your buyers, peers, and tastemakers already hang out, adding perspective that keeps your brand top-of-mind or reinforces your unique POV.

7. Tracking & learning: close the loop by measuring impressions, followers growth, engagement rate, but also newsletter sign-ups, which formats/topics convert best, and influence on pipeline. Then, feed those insights back into your next research sprint.

Nail this rhythm and every post becomes a data point that sharpens the one after it — compounding reach, credibility, and pipeline over time.

1) How Taplio streamlines research & strategy

Now that we’ve got the core components of the LinkedIn content lifecycle, here’s how Taplio can help you streamline each of them. First, let’s start with research and strategy.

Taplio helps you turn LinkedIn from a never-ending scroll into a structured research engine. Here’s the flow I run before writing a single line of content:

• Topic & format discovery: Navigate to the Inspiration tab to search for relevant keywords, star stand-out posts, and build a living swipe file. I prefer to limit results to the past 15 days to optimize for recency and ignore follower counts to discover posts from emerging accounts instead of the largest, most established ones.

  

• Create curated contact lists: Then, I use Collections to organize each post or account find into collections by content pillar (topic) or winning format, one click at a time. I use lists to keep track of ICP buyers and niche tastemakers to dedicated lists. You could also segment these lists by client brand so every feed instantly reflects the trends and nuances of every industry you’re working on.

  

• Chrome extension on the go: when on LinkedIn and you land on a cool profile, you can also use Taplio’s Chrome extension to surface the account’s top-performing posts, and star both the profile and its top content into your Taplio lists without leaving LinkedIn.

  

BONUS. Export for deeper analysis. You can also export your saved posts or contacts from your collections into your preferred LLM to reverse-engineer hooks, spot pattern breaks, or uncover correlation between format and engagement. Use the prompt below to run your research and strategy analysis.

```

<ROLE>

Act as a top-tier content intelligence analyst and copywriting strategist.

Your specialty is reverse-engineering social media content (specifically LinkedIn posts) to detect patterns in format, hooks, tone, and structure that correlate with high engagement. You excel at breaking down viral and underperforming posts into components, and translating raw post data into concrete content insights.

<TASK>

- Analyze the provided exported LinkedIn posts or profiles (in CSV format).

- Break down each post into the following elements:

* Hook type (question, contrarian, story teaser, stat lead, bold claim, etc.)

* Format (text-only, carousel, poll, document, video, image, etc.)

* Tone of voice (personal, educational, inspirational, humorous, provocative, etc.)

* Structure (how many lines before CTA or shift in post, story arcs, etc.)

* CTA type (comment bait, lead magnet, share prompt, none, etc.)

- Tag patterns or outliers where the post significantly overperforms or underperforms based on engagement (likes, comments, shares, impressions).

- Identify what correlates most with high or low engagement:

* Does post length correlate with performance?

* Which hook types repeatedly win?

* Which format outperforms plain text? Polls, carousels, images, or videos?

- Highlight pattern breaks—posts that diverged from the author's norm and either flopped or took off.

- Suggest 3 key content hypotheses the creator could test based on this analysis.

- Provide 5 hook formulas derived from the most successful posts, ready to plug into future content.

<GOAL>

The goal is to reverse-engineer a set of LinkedIn posts or profiles to uncover replicable patterns and strategic content decisions that drive above-average engagement.

<STYLE AND TONE>

- Use clean, analytical, professional language.

- Keep it insight-driven, using concrete examples and clear comparisons.

- Structure output for clarity: use headers, bullets, and charts/tables if needed.

- Be neutral in tone—objective, not flattering or overly critical.

<OUTPUT FORMAT>

- Summary insight section

- Breakdown table or bullet list per post

- Correlation matrix or bar chart (if engagement stats are numeric)

- Pattern break highlights

- Final content recommendations and hypotheses

<INPUTS>

- Exported LinkedIn post or profile data (CSV, Notion, Google Sheets, etc.)

- OPTIONAL: Add 1–3 posts that performed exceptionally well or poorly and you'd like dissected in more detail

- OPTIONAL: Add your current post structure or brand voice doc to help with calibration

```

Come up with your analysis prompt own using my very own Genesys prompter.

Create your own prompts

No matter how time-consuming this research process can be, rest assured it compounds — keeping track of the hot people, content, and formats your ICP are moved by forces to keep your juices flowing and keep you relevant in a fast-moving industry.

Product notes for the Taplio team: Taplio would be even better if you could bring richer account analytics into the core UI — think things like posting cadence, common posting days/times, format mix across images, text-only, or carousels, engagement benchmarks; and even advanced filters for segmentation (eg. by media type, follower brackets, language, and country). That data would close the loop between research and planning even more, all in one pane of glass.

2) How Taplio streamlines writing, collaboration, and distribution

What I like about Taplio is that, once research and strategy are squared away, you can switch straight to creation mode — turning a tangled content pipeline into a smooth production line. Here’s how I use Taplio’s main features to streamline my content creation workflow:

• AI tools settings: In Taplio’s AI tools, you can enter your brand kit in settings to personalize the content created to your (or your client’s) business. Why is this important? Because as I recently wrote here, one of the biggest rules of AI is Garbage in = garbage out — meaning that, without grounding the AI in proper context, it won't give you good output. That’s why it’s critical that you provide Taplio with this context. And if you don’t have anything at hand, you can get your own brand kit with my Genesys Brand Kit generator to get a headstart. Then, you can refine and paste the output in the AI tools setting sections.

• AI tools: now that the AI is grounded in some context about your writing style, you can use the AI tools to:

  • Come up with fresh post ideas

  • Or drop a blog paragraph, and Taplio will expand on it to create a 90% ready LinkedIn post. What I like about this feature is that you can also tweak the style and tone of each post, choosing from a few presets.

    

  • Propose scroll-stopping hooks

  • Repurpose long-form pieces into bite-sized LinkedIn posts

    

  • And even auto-build carousels that match your colours and fonts. Just pick your brand colors, customize the look and feel, and insert your copy — all without switching between Figma or Canva.

• Drafts kanban board: once you have your post drafts ready, you can drag each of them through a custom kanban board with columns that mirror your very own workflow. I normally keep the following states: Backlog, In progress, In design, In review, Ready to publish, Published. Again, no more rogue Google Docs or Notion tables — now you have everything streamlined, which is a real benefit if you’re doing this at scale.

• Queue: Once a draft is green-lit, I drop it into Taplio’s scheduler and pick the exact day and time you want it to go live. Your calendar shows everything at a glance, so you can spot gaps, avoid overlaps, and keep a steady cadence without manual reminders. You can even tap into Taplio’s own data to check the best performing times for your own geo, which can be pretty handy to run your tests.

  

  

• Collaboration: And if you’re working with clients or teammates, you can push any draft straight into their Taplio workspace for final sign-off. Likewise, clients can send posts back to you for tweaks and approval, keeping feedback loops tight and version control painless on both sides. Note: they do need to have a Taplio paid account to make this work.

Product notes for the Taplio team: if the AI tools tab was housed under a dedicated “writing” tab the UI could reflect an even more natural and integrated LinkedIn content lifecycle. Plus, if you could bundle the current AI tools with proven or trendy content templates (hook + body + CTA) with editable tone-of-voice guidelines, that would make the content creation process even more tailored — giving you the option to build your very own combination of differentiated content. Oh, and also being able to pull multiple clients’ posts into drafts or queues would make multi-brand orchestration feel as seamless as a single feed.

Want me to write a deep dive similar to this for your product? I love deep diving and using the latest SaaS and share my learnings and tips with my audience. Let’s chat!

3) How Taplio streamlines engagement

As discussed above, posting on LinkedIn is only half the battle — the real reach compounds when you comment, and Taplio makes this process effortless. In Taplio’s Engage tab, you can:

• My post comments: see every new comment to all of your posts in one feed, so you can fire back fast, and ensure no prospect slips through the cracks. Remember, replying to each comment on your own posts will double the number of comments and increase your reach, especially when tagging commenters.

• With people (your lists): filter the comment feed to just your ICPs and tastemakers’ lists, so you can add thoughtful takes on their posts and stay top-of-mind without doom-scrolling.

  

• Best for you: Taplio also surfaces net-new conversations based on the keywords and interests you set, letting you expand reach beyond your immediate connections.

• Auto-like and comment suggestions: you can also tap once to drop a response and a subtle like, signalling you’re engaged without breaking flow (and yes, ALWAYS layer your own voice on top — please never use a 100 % AI-canned reply).

Product note for the Taplio team: being able to tag people or add images in the comments directly from within Taplio would make this tab even more powerful. Also, you could customize different people’s lists to prioritize who to engage with depending on your latest focus, and hide comments you’ve already addressed to so the suggestion feed stays fresh and you keep more focused.

4) How Taplio helps tracking post analytics

Your content only improves at the speed you can learn from it, and Taplio puts those insights in plain sight:

• Unified performance dashboard: go beyond native LinkedIn views and reactions by pulling metrics like post’s likes, comments, shares, profile visits, and follower growth into a single view.

  

• Deeper winners list: pin top-performing posts or tag stand-out hooks, visuals, and formats so you can spot repeatable patterns without trawling spreadsheets, and feed directly to your repurposing machine and queue.

• Export for more analysis dives: as discussed before, make sure to export your post engagement data into your favourite LLM to mine correlations between hook style, post length, and conversion lag. Use the prompt above, refine it or create your own with my GPT prompter.

Product note for the Taplio team: adding a time-series overlays that help tie metric spikes directly to specific posts could give operators instant clarity on what moved the needle — and native AI-suggestions that understand mine your and others’ posts context could help extrapolate hypotheses as to why that post over- or under-performed.

Closing thoughts

LinkedIn rewards operators who do content with consistency, relevance, and a unique POV — but trying to hit all three as a team of one can feel like sprinting on a treadmill. Taplio help streamlining that sprint into a flywheel:

• Research the right people, topics, and formats without drowning in the feed.

• Create on-brand posts fast, then move them through a clean Kanban and scheduler.

• Engage in the comments that matter with your and others’ posts.

• Track the right KPIs so the next post is better than the last.

I wrote this deep dive with Taplio’s support, yet every suggestion and wishlist item comes from my own daily grind as a solo consultant and my experience using the product.

If you want to see how streamlined your LinkedIn can get, give Taplio a spin — and use code MATTEO1X1 at checkout to get 1 month of their Pro plan for just $1 dollar. Try the workflow, measure the lift, and drop me a note with your wins (or gaps) so we can keep this content loop compounding.

You need:
→ To focus on a relatively narrow audience
→ Develop a contrarian point of view to go against the grain
→ And balance trendy formats and story arches to match some of the algo rules

Look at Linear's marketing:
→ They speak only to engineers and product teams
→ While others chase scale and volumes, they focus on quality and craft
→ They still do some "trendy" stuff: funding announcements, AI agents products, case studies, office hours, etc.

But they do these things "their own way".

And they cared to define their own way very well.

In a world of mediocrity, your own way of doing the best practices is what stands out.
- Not just fitting into the trend
- And neither over-indexing on being an outsider

Balancing differentiation and trendiness is the key.

How are you doing it?

Let me know in the comments.

Just insert the URL of your company.

And you'll get a structured brand kit complete of:

- Brand overview & mission
- Product overview, capabilities, and benefits
- ICP
- Competitors
- Product differentiators
- Brand narrative
- Social proof
- Writing style & rules
- Top content
- Brand objection & responses
- CTA

What do you do with it?

1) Add it as *Instructions* to your GPT folders or LLM chats to ground the AI with context
(I use a dedicated project folder for each of my clients with these kits)

2) If you're using AirOps, they have a dedicated brand kit for the same use case
(they've been my inspo for this GPT 🤝)

3) You can now have leaner prompts and streamline your workflows faster
(without variables like audience, style, tone)

___

Want to try it out for your company or clients? Check it below ;)

Try Brand Kit Generator

And don’t forget to share your feedback in the comments!

PS. I'll be releasing one custom GPTs every week for at least the next 5-6 weeks.

He argues that:

↳ "Technology makes it easier to build, but harder to care (for the craft)"

↳ "We replaced purpose with metrics"

↳ And we delegated "judgment to AB testing"

And he thinks that the reason for that is that:

↳ "When you can do more and focus on the process"

↳ You can distance yourself from understanding "what's the right solution"

Here are my two cents on these claims:

1) At its core, technology is a deflationary force

• It IS about *doing more with less*

• It's the designer's job to intimately know a process' status quo

• Abstract it's key components and first principles

• And design an incrementally or radically better way of conducting such process

So NO — I don't think it's technology's fault that we lose focus on the craft.

2) I think it's about the *market* dynamics

• Tech VCs demand VOLUME, SCALE, and NOW > quality

• This begets speed to win over competition and maximize returns

• And AI (extremely deflationary tech) lowers barrier to entry

• Compounding this frantic competition, and speed to win over others

It's the capitalistic nature of tech and its hyper-competitive dynamics that I think are at the core of our frantic race towards metrics in spite of craft/quality

(not saying it's a bad thing but this is more like it than tech itself)

3) In this market, QUALITY = fx(CRAFT) = DIFFERENTIATION → because it's scarce

BUT it's not abstract, even Karri shows it in his slides:

• Linear has tenets on quality coming from their customers — who qualify it as ↓

• Intentionality, seamlessness, speed, personal, and scale (all proxies of "quality")

• Seamlessness = knowledge of the customers' process and experience

• Intentionality = developing opinions that show you've empathized with customers

• Personal = making the customer feel listened to (back to empathy)

• Speed = deliver solutions based on intentionality, fast (eg. support)

• Scale = the whole team delivers with consistent intentionality and speed

↓↓↓

4) From quality = fx(craft) to TASTE

• Quality is not abstract but RELATIVE because it depends on TASTE

• Which is determined by the tastemakers of a specific sector/industry

• Tastemakers are those who turn deep understanding of the market into strong POVs

• That's why Linear has emerged as a mature, hyper-competitive market

• They deeply understood the proxies of quality *their customers care about*

• And baked them into their product as "craft" and in their marketing as "taste"

So in the end, I believe

1. In tech, you must have speed, do volumes, and deliver numbers to play the game

2. B/c of market dynamics, while most catch up w/competition, craft and taste get lost

3. But if you want to play the long game, you must layer craft and taste on top

4. Craft shows you know how to design a different, better quality product

5. And taste shows that you know how to brand it and market it differently

PS. Thanks Karri for the inspo on this rabbit hole — big fan of Linear!

THE BACKSTORY

May 2022

- Got fired from OnDeck (ODF) after only 4 months (layoffs season)

- Lost 80% of my net worth in the crypto bubble burst (lol)

- Almost break up with my now fiancé

Everything happened in one, intense week BUT:

- I knew I didn't want to get another FTE

- Had done 1-2 consulting gigs before

- Had enough experience to try my luck

So I refreshed my LinkedIn, and put myself out there.

(While started doing couple therapy lol)

Things took a rapid twist:

- Found my first client from the ODF portal (ironic)

- Then another one came from a uni friend

- All of a sudden, I was floating

And so it began.

Y1 strategy

• Positioning: generalist marketer

• Target: any tech startup

• Channels: personal network

• Model: retainer by hours

• Goal: keep floating

EOY '22 (7 mos in):

- Clients: ~3/mo (started writing HyperGrowth Partners NL)

- Days worked: ~14/mo

- Revenue: 110% of my prev. job salary

Things to work on:

→ Get to "at capacity"

→ Get better, more known clients (++ social proof)

→ Work with clients who value quality work

Y2 strategy

• Positioning: generalist, B2B SaaS only

• Target: seed to Series As

• Channels: personal network

• Model: retainer by deliverables

• Goal: go upmarket (Series A)

EOY '23 (per quarter):

- Clients: ~5/m (landed first Series A SpotDraft!)

- Days worked: ~17/mo

- Revenue: 178% YoY

Things to work on:

→ Niche down positioning/target

→ Keep at it with content, start posting on LinkedIn

→ Let go of non-fit clients

Y3 strategy

• Positioning: B2B SaaS product/content marketer

• Target: Series A-B (some seed)

• Channels: personal network, LinkedIn

• Model: retainer by deliverables

• Goal: go upmarket, grow brand/content

EOY '24 (per quarter):

- Clients: ~6/mo (Owner.com, Ahrefs, Toast, AirOps, Ethena, Common Room)

- Days worked: ~18/mo

- Revenue: 134% YoY

Things to work on:

→ Automate, streamline, and productize

→ Consistency with content/social proof

→ Say 'no' more (hardest part)

→ More strategic collabs, content, partnerships (eg. GTM Engineer School)

WHAT KEEPS ME GOING?

I don't need/want to:

- "dominate the market"

- get to $$XXM ARR

- be the best at X

What I care about is:

→ Financial freedom: maintain or improve my lifestyle

→ Location freedom: work from where I want

→ Time freedom: retain leverage on my calendar, choose who to work with

→ Fulfilment: do something I enjoy, keep learning, and feel alive

→ Optionality: own my choices, and be able to change them

→ Equity: build my own, not someone else's

Without this set up, it'd have been much harder to:

- Take ~40 days off p/y

- Work from Tokyo or Rome 1-2 months p/y

- Quality time with my 101 yo grandpa

- Spend her bdays with my Mom

- Celebrate my old man's retirement in Patagonia

This post is to say: I'm damn grateful!

⤷ And if I made it this far, you can do it too!