AI Services · Method
Production systems in weeks, proven before they ship.
Senior architects set the rules. Our engineers build with AI agents. Checks the agents can’t change decide what counts as done, and every release ships its evidence.
The delivery method
1. Architecture ratified first
Senior architects work with you to set the requirements, the threat model, the architecture decisions and the launch thresholds. Independent review agents challenge each one before anyone signs off.
2. Acceptance tests frozen before code
Every requirement becomes an executable acceptance test, written independently of the code that will satisfy it and frozen before the build starts. You approve the specification and the tests.
3. Engineers and AI agents build in parallel
Our engineers run agents in three lanes at once: one builds, one checks the deployed system is ready, and one audits the code. No change merges until it passes every check and an independent review, the agents can’t edit those checks, and an engineer approves every merge.
4. Integrations proven before go-live
Checks prove each increment. Test runs on real data and small, capped live runs prove how the pieces work together, because that’s where defects hide.
5. Evidence on every release
Each release ships its proof: acceptance-test results, mutation score (how many planted bugs the tests catch), security scan, component list and AI cost per feature. A named person approves every merge.
Six rules behind everything we build
The model is the last, smallest step
Code filters, parses and fixes everything it can first. The model handles what’s left.
Done is decided by tests, not by the agent
Done means passing tests written independently of the code and enforced by checks the agent can’t edit.
Hard stops sized to the cost of a mistake
Hard controls go wherever a failure would be costly and can be detected. Everywhere else, agents get clear guidance, because over-constrained models do worse work.
Every routed call measured
Every call through our gateway is traced and costed, a nightly AI judge samples quality, and budgets stop work when they run out.
Limits live in code
Customer-data limits, approval rules and spend caps are enforced by automatic checks, not policy documents.
Expertise packaged for the agent
Our engineering standards are written as skills the agent pulls in for the task at hand, so no session starts from a blank prompt.
Every number, from our production estate
~10,000
commits in an AI-built codebase our engineering pipelines run under checks that let known-problem counts fall but never rise
~1,900
pull requests merged across our contract-first builds, each reviewed by an engineer and gated by checks our agents can’t edit
10 days
from a 187-agent review of our AI-built code to every confirmed issue fixed, critical and high-severity included
1,400+
code-review items fixed by our AI fix agent, at as little as $0.09 each
3.3%
of the SEC filings our monitoring pipeline reads ever reach the model; plain code does the scoring
44%
under-count in AI cost reporting, caught because two independent meters check each other
21
engineering standards and about 480 rules written for agents, in daily use across our estate
Live credential
caught by our adversarial second pass after first-pass scanners marked the code clean
Anyone can run an agent. Making it safe is the work.
The major coding platforms now ship their own agent loops. What you can’t buy off the shelf is everything around the loop: spend caps, guards that block when unsure, adversarial checks and agent-run reviews. Add the judgment of where to constrain a model and where to leave it free. That wrapper is what we build and install for you.
See the method on your problem.
Book the 30-minute live demo, or tell us what you need built, measured or governed and we’ll walk you through each step.