A GTM diagnostic for founders who built the product but still need the market to move.
About me
I have spent the past years taking ideas from concept to early traction across startups, SaaS, e-commerce, and AI.
I have built and scaled e-commerce businesses, led marketing and growth for a fintech startup, built an AI mental-health venture that reached thousands of users and raised public and private funding, and helped build a 23,000+ community across Europe.
I hold a PhD in developmental psychology and have spent years building and growing products. That combination makes me look at GTM through behavior, motivation, trust, and decision-making — not channels alone.
Why this exists
AI made products easier to build. It did not make distribution easier. Most founders do not need another list of channels. They need to know which adoption problem they are solving. The playbooks are not stories to copy blindly. They are pattern libraries for choosing the next experiment.
Why URL-first
Your homepage is the version of your pitch the market actually sees. Reading it first costs you nothing and surfaces the gap between what you claim and what a stranger can believe. Long forms make you rationalize; a URL makes you honest. The three follow-up questions come after the first-pass result, not before it.
How the matching works
Rule-based, not a black box. We read the page for category, audience, pricing, proof and workflow signals, pick the most likely adoption bottleneck, then score all seventy-five playbooks from 0-30 across six dimensions worth 5 points each.
- Problem similarity — Does this startup face a similar adoption, trust, or distribution problem?
- Audience similarity — Is the first audience structurally similar?
- Trust mechanism fit — Can this startup earn trust the way the reference startup did?
- Founder/channel fit — Can the founder realistically execute the motion?
- Visible proof loop — Can users, demos, artifacts, or public proof compound distribution?
- Timing/category fit — Is there existing market curiosity or a category narrative to ride?
24-30 strong fit · 18-23 partial fit · 12-17 weak fit · 0-11 poor fit. Below 18 we say the fit is weak and recommend a diagnostic experiment instead of pretending certainty.
The 75 researched playbooks
- Lovable — Expert-Proven, Beginner-Owned
- Cursor — Familiar Workflow, Expanding Autonomy
- Perplexity — Inspectable Trust, Repeated Habit
- Clay — Operator Superpower, Community Proof
- Replit — Zero-Setup Creation Loop
- Duolingo — Free Habit, Cultural Flywheel
- Cal AI — Camera Utility, Creator Portfolio
- Partiful — Host-Seeded Invitation Loop
- Too Good To Go — Neighborhood Density, Surprise Urgency
- Strava — Single-Player Proof, Social Motivation
- Notion — Personal System, Creator Distribution
- Figma — Multiplayer Artifact, Team Pull
- Canva — Use-Case Intent, Template Expansion
- Airbnb — Concentrated Liquidity, Transaction Trust
- Finch — Emotional Companion, Gentle Habit
- Slack — Team Density, Organizational Standard
- Dropbox — Utility Referral, Shared Reward
- Calendly — Recipient Exposure, Creator Conversion
- GitHub — Public Work, Reputation Network
- HubSpot — Category Education, Tool Capture
- Shopify — Merchant Success, Partner Ecosystem
- Zapier — Integration Intent, Long-Tail Discovery
- Webflow — Showcase Proof, Expert Services
- Superhuman — Concierge Activation, Habit Qualification
- Substack — Creator Audience, Network Discovery
- Atlassian — Team Tool, Land-and-Expand
- Linear — Opinionated Craft, Pull Adoption
- Vercel — Deployment Moment, Developer Expansion
- Cloudflare — Free Edge Utility, Infrastructure Trust
- Twilio — API Primitive, Use-Case Expansion
- Stripe — Developer Love, Economic Embed
- Segment — Open Primitive, Category Bridge
- Postman — Solo Utility, Collaborative Standard
- Docker — Portable Standard, Ecosystem Pull
- Sentry — Pain Capture, Developer Habit
- Datadog — Wedge Monitor, Platform Expansion
- Snowflake — Workload Proof, Consumption Expansion
- MongoDB — Developer Model, Enterprise Standard
- Elastic — Open Core, Search Everywhere
- Supabase — Familiar Alternative, Builder Community
- Hugging Face — Open Models, Practitioner Network
- Miro — Workshop Artifact, Team Continuity
- Loom — Async Artifact, Recipient Pull
- Typeform — Beautiful Interaction, Embedded Distribution
- Airtable — Flexible Database, Department Expansion
- Asana — Team Workflow, Executive Visibility
- Monday.com — Visual Flexibility, Use-Case Packaging
- ClickUp — Suite Consolidation, Champion Migration
- Intercom — Messenger Wedge, Customer System
- Gong — Captured Reality, Management Proof
- Rippling — Employee Graph, Product Bundling
- Brex — Founder Wedge, Finance Expansion
- Ramp — Savings Proof, Finance Control
- Deel — Complexity Removal, Geographic Expansion
- Gusto — Critical Workflow, Human Trust
- Wise — Transparent Price, Trust Transfer
- Revolut — Feature Wedge, Financial Super-App
- Robinhood — Friction Removal, Participation Identity
- Coinbase — Trusted On-Ramp, Asset Expansion
- Etsy — Niche Supply, Buyer Intent
- Uber — City Liquidity, Reliability Habit
- DoorDash — Suburban Density, Merchant Operations
- Instacart — Operational Layer, Retailer Network
- ClassPass — Aggregated Variety, Capacity Yield
- Peloton — Premium Hardware, Live Community
- Headspace — Guided First Win, Wellness Routine
- Calm — Immediate Relief, Content Depth
- Spotify — Free Listening, Personalized Habit
- TikTok — Instant Feed, Creator Feedback Loop
- Pinterest — Intent Collection, Commerce Bridge
- Reddit — Niche Community, Contextual Discovery
- Discord — Community Server, Persistent Belonging
- Twitch — Live Creator, Participatory Audience
- Patreon — Core Fans, Recurring Patronage
- Kickstarter — Campaign Proof, Backer Commitment
Why the result is a hypothesis, not a truth
The diagnosis is built from public page signals and your own answers. It states its assumptions and tells you what would make it wrong. Treat it as the fastest available starting hypothesis. The goal is not the best GTM strategy. It is the next testable GTM move.