A demo that works once is not a product. The gap is evaluation: knowing what the system gets right, catching the day a prompt change quietly makes it worse, and being able to show a customer why an answer is what it is. We build for that gap first.
Retrieval over a validated semantic layer rather than raw tables, so generated figures trace back to a query a human can read. This is the difference between an analytics copilot a finance team trusts and one they stop opening.
Agents that call your systems through explicit, permissioned tools, with every action logged and reversible where it needs to be. Autonomy is scoped deliberately rather than hoped for.
A graded test set that runs on every prompt, model or retrieval change and blocks the merge when quality regresses. Without this, model upgrades are a coin flip you perform in production.
Input and output filtering, cost and latency budgets per request, and tracing that lets you reconstruct exactly what the model saw when it produced a given answer.
Warehouse modelling, vector storage and the pipelines that keep both current. Most AI projects that stall are data projects that were never finished.
Whichever fits the task, the latency budget and the data-residency constraint — frequently Claude for reasoning-heavy work. We design the integration so the model is a swappable dependency rather than an assumption baked through the codebase.
By not letting it produce them freely. Generation is constrained to validated queries against a semantic model, every figure is traceable, and the evaluation suite fails the build if a change starts producing unsupported answers.
Yes. We deploy into your cloud accounts, and where data residency or policy requires it we design around self-hosted or regionally-pinned inference.
Tell us what you are building. You will hear back from an engineer, not a sales development rep — usually within one business day.
San Francisco, CA · Serving clients in 30+ countries