Autonomous agents, engineered to run.

Because most AI projects don't fail in the model — they fail because the data underneath is unreliable, undocumented, or quietly broken. We start with the data layer (pipelines, contracts, quality checks, lineage) so the AI on top has something solid to stand on. Deploying agents on a broken data foundation produces confidently wrong outputs, which is the worst failure mode in production.

Both, and we strongly prefer both. We're data engineers first. A typical engagement begins with building or repairing the data foundation — warehouse, pipelines, contracts, quality — before any model or agent goes in. One team owns the whole stack so the seams between data and AI are not a handoff.

Automation is deterministic — a workflow that does the same thing every time, predictably. An AI agent makes a decision: it reads context, picks an action, and acts. We build both, and we'll often tell you that your problem is actually automation — which is cheaper, more reliable, and easier to operate.

Production only. We don't take one-week proof-of-concept engagements — a week isn't enough time to find the interesting failure modes, which means a week-long POC produces false confidence. We build systems your team can operate on a Sunday at 3am.

Industry-agnostic. Common patterns include fintech, healthtech, logistics, e-commerce, and industrial. The qualifier isn't sector — it's whether the company depends on data working, has real operators, and has a real production target.

Fixed-scope written proposals for the readiness audit and each build phase. We don't do pure time-and-materials and we don't bill for prototypes. Numbers are agreed in writing before any build work starts, and the engagement is structured in phases so you can stop after any of them.