Production AI,
built to research standards.

MTEB tells you which embedding model is good on Wikipedia. It says nothing about whether your domain-specific RAG works. We build golden sets from your actual documents and your actual queries — and we instrument hit-rate, MRR, faithfulness, and downstream answer quality with statistical confidence intervals.

Most RAG pipelines die at scale: stale indexes, embedder drift between ingest and query, batch jobs that crack on the first malformed PDF. We design drift-resistant embedding strategies, incremental ingestion that handles real-world file mess, and serving layers that hold up under real load.

When we leave, we leave behind a runnable harness — pytest-style — with golden datasets versioned in your repo. Anyone on your team can run it. Embedding model regressions catch themselves on every PR.

Every voice call gets a structured trace: transcript, model outputs, tool calls, timing per stage, latency by component. When a customer reports “the agent said something weird,” you replay the exact run instead of guessing. Foundation for any serious voice product.

Voice has unforgiving latency budgets — 300ms is when users start noticing dead air. We measure and tune p50/p95/p99 across STT, retrieval, LLM, and TTS. We design fallbacks so a single component failure does not end the call.

When voice AI breaks at 3am, the on-call engineer needs structured playbooks: symptom → check → mitigate. We write those playbooks based on the failure modes we have actually seen in production at multi-tenant scale.

Most agent demos work once and break the second time. We design test harnesses where the same prompt-environment-model triple produces the same outcome — with controlled stochasticity. You compare agent versions side by side and know which one is actually better.

Every agent run logs a full execution trace: tool calls, intermediate reasoning, token cost per step. When a single query costs $4 in LLM calls, you see it. When a planning loop spirals, you catch it before the bill arrives.

Agents fail at scale not because the model is not smart enough, but because the architecture is brittle. We design with planning separated from execution, tool routing decoupled from generation, and clear interfaces that survive the next ten tools you add.

Most teams report “our model is 5% better” from a sample of 50 examples — well within noise. We design eval sets sized for the effect you actually care about, with paired tests, bootstrap confidence intervals, and proper power analysis. You will know when a change is real and when it is noise.

Every model swap, prompt edit, and retrieval change runs through a regression gate. If quality drops below threshold on the eval suite, the PR doesn’t merge. You ship with the same confidence you ship typed code.

Dashboards die. The teams that ship reliable AI are the ones where every engineer can write an eval, every PR includes a result, and the eval set evolves with the product. We seed that culture before we leave.