LLM capability inside your stack,
not your data inside theirs.

Whichever wins on your task, at your privacy tier, at the lowest cost, and that changes per use case. We benchmark candidates against a golden dataset built from your real documents, then route each task to the cheapest model that passes evals. The architecture stays model-agnostic, so when a better or cheaper model ships, you switch behind an interface instead of rewriting.

For most enterprise use cases, answering from company documents, CRM data or policies, RAG wins: it is cheaper, updates the moment your content does, and every answer carries a citation. Fine-tuning earns its cost for style, structured-output consistency or narrow classification at very high volume. We often prove the RAG baseline first, then fine-tune only if the evals say it pays.

No. We use zero-retention API agreements verified in writing, redact PII before any external call, and log every payload that crosses the boundary. Where regulation or contracts demand it, we deploy open-weight models like Llama inside your own AWS VPC so nothing leaves your infrastructure at all, the model comes to the data, not the reverse.

Grounding plus measurement. Answers are generated from retrieved passages with citations, constrained to refuse when retrieval comes back empty, and validated against schemas for extraction tasks. A golden dataset scores accuracy and hallucination rate on every prompt or model change in CI, if a change regresses, it does not ship. Low-confidence outputs route to human review.

A number you get before launch, not after. We model cost per request from real token counts during the prototype, then engineer it down with routing to smaller models, prompt caching and response caching, typically cutting token spend 60–75% versus a naive single-model build. Each feature ships with a monthly token budget and an alert before it is breached.

Yes, if latency is designed rather than hoped for. We set a p95 budget per feature, stream responses so users see the answer forming within a second, cache frequent queries in Redis, and route latency-sensitive paths to faster models. Batch workloads like overnight document processing are separated so they never compete with the live UI.

Hosted APIs from Claude or OpenAI win on capability per dollar for most workloads, with zero infrastructure to run. Self-hosting an open-weight model wins when data may not leave your network, when compliance requires full audit control, or when steady high volume makes GPU economics favorable. Many clients run both: hosted for general tasks, in-VPC for the regulated path.

A working pilot on your own documents typically lands in about two weeks; production, evals, redaction, integration, monitoring, usually runs six to ten weeks depending on data sources and compliance depth. The architecture review is fixed-price, the build is quoted as a fixed project after it, and ongoing eval-and-optimization runs as a light retainer you can stop anytime.