How we engineer.
This is the operating model behind all three service lines. It is the work — not the pitch about the work.
The pod model.
Small pods, sized to the work. Two-week sprints. Daily client visibility. Acceptance criteria written before the sprint starts.
We don't run multi-quarter, fixed-scope waterfall builds. We will walk away from RFPs that require it.
Eval gates on every AI feature.
If a feature touches an LLM, agent, or AI-driven decision in the production path, it goes through an eval gate. We define the metric before we build. We don't call a feature done until the metric clears.
You see the eval set. You see the pass rate. You see the diff between the last run and this one. Receipts, not adjectives.
AI-assisted delivery.
AI-assisted coding is the default. Engineers operate with AI in the loop on writing, reviewing, refactoring, and testing.
Test generation, edge-case discovery, regression sweeps run alongside the engineer, not after.
Runbooks and decision logs are written in the flow of work, with AI assistance. They stay current. They don't rot.
Daily client visibility. We over-communicate. We don't over-staff.
How we recruit and train.
- —AI-fluency is the bar, not a bonus. Hiring screens test hands-on AI work, not just whiteboard problems.
- —Every hire goes through an internal AI-fluency curriculum on entry, and continuing education on cadence.
- —We invest in tools, eval infrastructure, and shared prompts. The bar moves up. We move with it.
What we refuse.
- —We don't staff bodies into seats without the fluency bar.
- —We don't run multi-quarter, fixed-scope waterfall builds.
- —We don't sell models. We sell products, software, and people who land work in prod.
- —We don't claim compliance or security certifications we don't hold today.