Five pillars. One bar.
The discipline of shipping AI-written code at production quality. Skip any one and the model writes you a demo, not a product.
- Pillar 01
Specification
Tight specs in. Shipped code out.
AI ships exactly what you ask for — including the wrong thing, fast. We invest the first 10% in scoping so the next 90% lands clean.
- Pillar 02
Context
The model sees what we curate.
Output quality is a function of input quality. We engineer what the model sees instead of pasting one function and praying.
- Pillar 03
Verification
Closed-loop feedback, not vibes.
AI-written code is regression-prone unless you give it a tight feedback loop. Tests, types, end-to-end probes — wired before the first feature, not after the first incident.
- Pillar 04
Architecture
Bets the model can't make for you.
LLMs converge on average. Boundaries, naming, abstraction — the bets that compound or rot a codebase. We make them on purpose, not by accident.
- Pillar 05
Hardening
The work AI doesn't add unless told to.
Secrets, observability, error handling, idempotency. AI ships demo-grade by default; production-grade is opinion plus discipline. We bring both.
“These five aren't a checklist — they're the framework that turns AI from a 2x intern into a 10x senior.
Skip any one and you're back to the intern.”