Autonomous Coding
with Claude CLI

Build enterprise systems without a team of developers.
We teach you how.

The Problem

Β£50k+

Cost to hire one developer per year

3-6 months

Time to build a team and ship features

100%

Dependent on keeping them around

There's a better way.

How It Works

1

System Documentation

We help you document your system architecture, APIs, database schema, and constraints in a CLAUDE.md file. This becomes your blueprint for autonomous building.

2

Feature Specification

You learn how to brief Claude CLI what to build. Clear specs β†’ Claude builds exactly what you need. No ambiguity. No rework.

3

Autonomous Build

Claude CLI reads your docs, understands the system, and builds features autonomously. Wired, tested, documented. Ready to deploy.

4

Deploy & Scale

You ship it. Iterate faster. Build more. No hiring. No onboarding. Just Claude building autonomously.

Proof: Built This Way

πŸ€– AI BUILT

Taming AI

Intelligent compaction monitoring for construction. Real-time analysis, autonomous documentation, live deployment.

βœ“ 5 modules (Admin, WOLD, Trail, Tools, System)

βœ“ 1,431 trail objects managed

βœ“ Complex RBAC (8+ role types)

βœ“ Supabase + Vercel + Real-time sync

βœ“ Live in production

πŸ€– AI BUILT

Clean Code Autonomous Coding

Event-driven platform. 14 integrations. PostgreSQL queue. Fully autonomous.

βœ“ 14 event types (weather, tide, flood warnings, rail delays)

βœ“ Free API integrations (Open-Meteo, OSRM, Environment Agency)

βœ“ Async processing with Lambda functions

βœ“ Standard result envelope for all events

βœ“ Fully documented for Claude

Both built autonomously using Claude CLI. Both fully deployed and scaling.

πŸ’»
FRONTEND

Next JS Web

An AI coded Frontend

Next.js simplified. Same clean patterns as the backend.

β†’ Commands layer (clean)

β†’ Use cases layer (clean)

β†’ Repository layer (clean)

β†’ Single entry point envelope

β†’ Autonomous and extensible

βš™οΈ
BACKEND

Python Backend

An AI coded Backend

Clean architecture. Single entry point envelope. Consistent patterns throughout.

β†’ Commands layer (clean)

β†’ Use cases layer (clean)

β†’ Repository layer (clean)

β†’ Single entry point envelope

β†’ Autonomous and extensible

Why Documentation Matters

The Real Cost of AI Hallucination

1. The Problem

A developer (Rob) had broken environment setup. Staging, preview, and main branches weren't properly isolated. Different Supabase projects, different Vercel deployments, all tangled together.

But Claude didn't see that.

2. Claude's Hallucination

"Create a brand new project from scratch."

Claude couldn't debug the actual problem. So it defaulted to: "Start fresh. It's cleaner." Claude even wrote two justification documents explaining why recreating was right.

Result: Weeks wasted. Original codebase abandoned. Same problems resurface in the new project.

3. How Good Documentation Prevents This

With a clear CLAUDE.md:

## Environment Setup

Three environments: main, staging, develop
Each has separate Supabase + Vercel

Current issue: staging env vars not syncing

To fix:
1. Verify .env files match Supabase refs
2. Check Vercel staging config
3. Run migration 056 on staging DB

DO NOT create new projects.

Claude would:

  • Understand the existing architecture
  • Know exactly which files to check
  • Debug and fix the actual problem
  • Never suggest recreation

Good documentation = Claude builds right
Bad documentation = Claude hallucinates

Why This Works

πŸ’° Saves Money

Build systems for Β£5k-20k that would cost Β£50k-200k to hire engineers for.

⚑ Ships Fast

Weeks instead of months. Iterate, build, deploy. Repeat.

πŸ“š Knowledge Transfer

Good documentation = anyone can understand and build on your system.

♾️ Scales Infinitely

Claude can build anything if documented well. No hiring ceiling.

Get in Touch

Questions? Want to discuss your system? Reach out directly.

Send Email

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