Last week, I finished what might have been the most interesting research project of my life.

The official Lean AI leaderboard
For the last three months, I've been obsessed with finding out how some companies are generating millions in revenue with teams so small that it should be practically impossible.

After having countless conversations with 50+ founders who are actually pulling this off, I wrapped it all up in one place.

I am happy to present the complete playbook these companies use to build, scale, and dominate their markets with skeleton crews.

Now, you might wonder why I'm sharing this instead of hoarding these insights. Fair question.
I want to do something useful with what I've learned.

There are millions of founders grinding away, working 80-hour weeks, raising round after round, slowly losing their souls to build companies the old way.

I've been there. It's exhausting and unnecessary.

These lean AI companies have found a better path. One that preserves your sanity, your equity, and your bank account.

Everything you are about to read comes directly from the founders who are living it.

Ready? Let's dive in.

Their operational workflows:

After 3 months of pestering founders, sitting through demos, and diving into their actual operations, here's what I discovered:

These companies have built entire operational systems that multiply human capability.

Snapshots of workflows of a company on the Lean AI leaderboard

Customer Support:

  • Haven AI's bots handle 50% of tickets autonomously

  • Multiple companies process dozens of user interviews in a day (yes, one day)

  • Stan generates 100% of creator products from social media in minutes

  • ArcAds' support agent checks the knowledge base, responds automatically, and escalates only 20% of tickets

  • AI Dungeon's UGC Content Reviewer reduces misrated content while increasing moderation coverage

Finance:

  • Haven auto-categorizes over 80% of transactions

  • They reconcile 90 %+ of accounts across thousands of customers

  • When one client buys Hubspot, the system learns and categorizes it for every future client automatically

  • Their prepaid expense detection proactively flags potential issues before accountants see them

  • ArcAds' AI Accountant pulls receipts from Gmail → categorizes with Claude → forwards everything clean to finance

Marketing and Sales:

  • Open Art cranks out hundreds of web pages weekly

  • One part-time person manages hundreds of SEM campaigns using OpenArt for creatives + Deepseek for copy

  • ArcAds' spy agent scrapes competitor ad libraries → feeds best into o1 → auto-generates viral videos

  • Their ghostwriter scrapes their top-performing tweets → suggests daily ideas → sends to Notion

Development:

  • Every founder mentioned that Cursor is tripling their coding speed

  • Open Art supports millions of monthly active users with a tiny engineering team

  • Many of these companies run production without QA teams, using Checkly for backend + Stably for end-to-end testing

  • Stan built entire platform with 3 founding full-stack engineers

  • ArcAds generates new keywords → creates landing pages with Claude → auto-adjusts bids daily

  • AI Dungeon's Bug Capture and Triage Agent monitors social channels for new issues and reduces the bug capture process time.

  • Their QA Agent analyzes code changes and generates comprehensive test plans

Their Tech Stack:

Here's where founders got surprisingly candid. Most wouldn't share workflows, but they named names:

Tech stack of a company on the Lean AI leaderboard

Customer Support:

  • AI Support Agents: Systems that pull from knowledge bases to answer common questions

    • ZenDesk AI, ChatGPT API, custom knowledge base integrations

  • Email Management: AI-assisted drafting and response with human review

    • Serif AI (70 %+ draft acceptance rate), Gmail API integrations

  • Feedback Analysis: NLP systems for synthesizing customer insights at scale

    • Claude for summarizing customer insights

  • User Research: Automated transcription, tagging, and insight generation

    • Dovetail for transcription and synthesis

    • Fathom for call analysis

  • Content Moderation: AI-powered systems for enforcing content policies

    • Cinder for content safety

    • Automated image and text screening

Finance & Operations:

  • Transaction Processing: Pattern recognition systems for categorization

    • Haven AI's pattern recognition systems

  • Document Processing: OCR tools for invoice and receipt extraction

  • Reconciliation Systems: Auto-matching algorithms integrated with accounting software

  • Expense Management: Gmail receipt extraction

    • Claude for categorization

    • QuickBooks/Xero

  • Finance Tools:

    • Harvey for legal

    • A haven for accounting automation

Marketing & Content Creation:

  • Content Generation:

    • OpenArt for images/videos

    • ChatGPT for creative writing

  • Influencer Marketing:

    • Beacons AI for matching

    • Gmail for emails

    • Mightyscout for tracking

  • SEO Tools:

    • Ahrefs

    • SemRush with custom AI analysis layers

Content generation workflows for a company on the Lean AI leaderboard

Sales & Lead Generation:

  • Lead Enrichment: Automated data collection from multiple sources

    • Apollo[.]io

    • LinkedIn scrapers

    • Custom enrichment via Claude/GPT-4

  • Outreach Automation: Personalized communication at scale

    • Custom LinkedIn messaging via Claude

  • Sales Analytics: Revenue intelligence through automated data collection

    • HubSpot with AI layers

    • Custom dashboards with GPT-4 analysis

  • Meeting Tools: Transcription and actionable summary generation

    • Granola for meeting management

    • Fathom and GPT-4 for extracting key points

  • CRM Enhancement:

    • Attio (pulls emails/calendars automatically)

Development & Engineering:

  • Coding Assistance: AI pair programming tools that triple developer output

    • Cursor (3x developer output)

    • GitHub Copilot

    • WindSurf

    • Claude Code

  • Vector Databases: Semantic search and knowledge retrieval systems

    • Pinecone for semantic search

    • Weaviate

    • Chroma for embedding storage

  • Event Processing: Event streaming and processing systems

  • Infrastructure Tools: Modern API and deployment infrastructure

    • FastAPI and Django for Python backends

    • Next.js API Routes for JavaScript backends

    • Vercel or Render for frontend

  • Admin Tools:

    • OnePassword

    • WhisperFlow for dictation

    • N8N for workflow automation

    • PandaDoc for contracts and signatures

  • Documentation: AI-generated technical documentation with human review

Where things break:

Around $2-3M ARR, many companies I spoke with hit the same wall.

Marcel from GrowthX showed me a $20,000 monthly bill from AirOps when their workflows became too complex.

That's when they realized they needed to build their infrastructure.

The pattern repeats everywhere:

  • No-code tools work great for prototyping

  • They completely fail at scale

  • Workflows that took 30 minutes suddenly take 2+ hours

  • Simple changes require weeks of refactoring

  • Human errors multiply as people duplicate workflows for different clients

That's when companies start building custom solutions.

Expensive and time-consuming, but necessary.

A founder told me they didn't write a single line of code until they were almost at $2M ARR.

But at $3M, everything broke for them.

They built their platform:

  • Reduced workflow execution from 2 hours to 2 minutes

  • Enabled real-time refactoring for new AI models (like GPT-4.1)

  • Created evaluation frameworks to measure AI output quality

  • Eliminated human errors in workflow duplication

Organizational design and hiring talent:

When it comes to running these lean AI companies, 90% of people assume these are lifestyle businesses run from Bali.

And trust me, nothing could be further from the truth.

"We are grinding hard at our offices," one founder told me bluntly.

It's just that these entrepreneurs have found a better way to build without giving away control and dilution. They work equally (if not harder) than you and I.

Core Hiring Principles:

  • Only technology-agnostic full-stack engineers

  • Generalists who think from first principles over specialists

  • People who can rapidly adapt to new AI tools

  • Fewer, better people with massive ownership

As Stan's Vitalii Dodonov put it: "When you hire the best people, they'll leverage AI to excel at their jobs. You don't need a hundred. Ten is enough."

The Global Talent Strategy:

  • Remote-first hiring expands the talent pool dramatically

  • International team members cost 50-70% less than US equivalents

  • Transparent compensation with growth paths regardless of location

  • Bar is extremely high: 7-step interview process, including assignments and reference checks

Team Structure Evolution:

As these companies scale, they organize differently:

  • Pod structure: Small, cross-functional teams with end-to-end ownership

  • Managing director model: Leaders who manage teams, bring in business, and develop practices

  • Engineering focus: Technical talent makes up a higher percentage of the headcount.

Representative team structure of companies on the Lean AI leaderboard

Adaptation Speed Advantage:

The ability to quickly adopt new AI capabilities becomes a competitive edge:

  • Prototyping new solutions within days of new model releases

  • Refactoring workflows for newer, better models in hours instead of weeks

  • Continuous evolution of systems to improve accuracy and capabilities

Implementation of best practice

1. Start with no-code, plan for custom

  • Use tools like N8N or AirOps for prototyping (all companies did this)

  • Plan for custom development at $2-3M ARR when things break

  • Document workflows as you build for easier migration

2. Measure everything obsessively

  • Track automation percentages by department

  • Monitor time saved vs. time invested

  • Calculate ROI on each automated workflow

3. Build for scale from day one

  • Design systems assuming 10x growth

  • Create modular workflows (easy to modify)

  • Plan for multiple models/APIs (avoid vendor lock-in)

4. Human-in-the-loop architecture

  • Never fully automate high-stakes decisions

  • Build confidence scoring for all AI decisions

  • Create easy override mechanisms

5. Documentation is everything

  • Document every workflow in detail

  • Create visual process maps

  • Record decision logic for AI systems

6. Build incrementally with regular feedback

  • Don't attempt to build end-to-end systems immediately

  • Implement in small chunks with frequent evaluation

  • Set up clear feedback mechanisms to improve performance

  • Create dashboards to track automation percentages and accuracy

7. Focus on data quality and context

  • Clean, structured data is essential for effective AI systems

  • Invest time in organizing existing information

  • Create clear documentation for internal processes

  • Build comprehensive knowledge bases before automation

8. Maintain human oversight and expertise

  • AI systems work best with human guidance and expertise

  • Create clear escalation paths for complex or uncertain cases

  • Design systems that learn from human corrections

  • Focus human time on high-judgment, strategic tasks

Roman Bodnarchuk
(416) 220-5314
www.n5r.com
www.10XClub.ai

10X Sales. AI First Success
Empowering our member companies to 10X their revenue with AI-driven sales and marketing automation—becoming AI First faster growth and profits.

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