How We Build: AI-Native, End to End
Most teams add AI to an old process. We rebuilt the process around it — AI in every stage of the software lifecycle, and a person's approval on everything that matters. We run our own company this way: a portfolio of thirteen products with no operations team, coordinated by our own agent control plane.
01
Plan
Intent, written down
Every change starts as a short written intent — what we want, why, and under what constraints — and gets an ID before any work begins.
What we actually do: Every change in our own codebase is traceable to a tracked ID raised before the work started.
02
Design
Requirements and design together
Requirements and design are worked out in one AI-assisted session, with our standards for security and quality applied as we go — so problems surface before engineering starts.
What we actually do: Our autonomous outreach engine was designed end to end, safety rules included, before any of it was built.
03
Build
A plan before code
AI drafts the implementation plan — which parts change, in what order, and how we'll know it works — and a person approves it before a line is written.
What we actually do: That same engine was built from a plan approved in writing, one tracked change at a time.
04
Test
Work that checks itself
Every change is checked before a person sees it — types, builds, automated tests, and a live run with real data where it matters.
What we actually do: A live run with real email found defects that reading the code had missed. All were fixed before any customer was affected.
05
Release
Gates, not trust
Automated checks run on every push, and anything irreversible — a production release, a message to a customer — needs a named person's approval. The system that does the work can never approve it.
What we actually do: Our outreach engine researches and drafts on its own, but it cannot send a single email. Only a person can release one.
06
Run
Production that reports back
Agents watch live systems. When a measure crosses a set limit, the system acts first — then the problem comes back to us as new, tracked work.
What we actually do: If complaint or bounce rates cross their limits, our outreach pauses itself. A watchdog on a separate platform tells us if a scheduler goes quiet.
The rules that don't bend
People own the judgement
AI handles routine work and routine decisions. People own intent, risk, and anything that can't be undone.
Separation of duties
Whatever writes the work can't approve it. Approval always sits with a different, named person.
A full trail
Every change links back to its intent, its plan and its review — so "why is this here?" always has an answer.
Docs that can't drift
When code changes, the documentation describing it has to change in the same commit — checked automatically on every push.
What this means if you work with us
- ✓You approve the plan before we build, so there are no surprises.
- ✓Every change traces back to a decision you can read.
- ✓Nothing irreversible happens without a person saying yes.
- ✓Problems are caught by the system that watches production, not by your customers.
Our approach follows the AI-native software development lifecycle described in Anthropic's AI-native SDLC playbook. We are not affiliated with Anthropic — we simply build this way.
Build with us
Tell us what you're building. You'll get a written plan before any code.