AI features that earn their keep. Data foundations that don't fall over.

We evaluate against three honest questions: does it solve a real problem the user has today, can it ship with a fallback that doesn't break the rest of the product, and is the cost-per-call sustainable at the scale you actually run at. If the answer to any of those is no — or unclear — we say so.

We don't replace data scientists; we ship the engineering layer they need to get models into production. Pipelines, retrieval, evals, observability, model versioning, on-call patterns. Most data science teams have a model that works in a notebook and no clear path to making it the thing customers depend on. That's the gap we fill.

Model-agnostic by design. Frontier models from the major providers, open-weight models on your own infrastructure, retrieval against your data — we pick what fits the eval and the cost envelope. We bias toward stacks your team can keep running without us, not toward whoever pays us a referral fee.

Six weeks for one production AI feature with evals and observability. Two-to-four weeks for an AI roadmap. Eight-to-twelve weeks for a data-layer rebuild. We don't do open-ended retainers for AI work — every engagement has a written end date and a defined hand-over.

We agree on the eval thresholds before we build. If we don't hit them, we tell you in writing — with the evidence — and we don't pretend otherwise. Often the right call is to kill the feature; sometimes it's to keep iterating; occasionally it's to change the underlying problem we're solving. We don't ship features the evals say aren't working.

Your data stays on your infrastructure unless we have a written reason to move it. We sign DPAs and NDAs by default, work to your regulatory environment, and don't train on your data for anyone else. For regulated industries (healthcare, financial services) we work to your audit standards from day one.