How we work
From Ground Truth to working software.
AI products require a shift from building features that work to calibrating systems that learn. We model the business precisely, derive specs from it, and let agents build under evals, guardrails, and human review.
The method
Knowledge
Everything we learn about your business, product, data, and constraints.
Ground Truth
A precise, validated model, owned, sourced, and kept current.
Specs
Capabilities and user stories with Given/When/Then acceptance criteria.
Agents + loops
Agents implement; evals verify; guardrails and humans keep it safe.
Working software
Production systems derived from the model, not demos.
The CC/CD loop
Two loops, not a straight line.
Continuous Development
Scope the next capability up the agency ladder, prove the logic, then build the application and add evals for it.
Continuous Calibration
Harvest real usage, run evals on live data, triage hallucinations and drift, and tune. No new code when a prompt fix works.
How we know it's right
Evaluation is a build artifact, not an afterthought.
A fix agent never verifies its own work. Behavior is observable in production: not just what it does, but how it behaves when no one's watching.
We dogfood it
This site is the proof.
Rootstrap's own website is built this way: a validated Ground Truth, specs and user stories, agents and loops, with human review at every gate. Structure before scale.