LLMs, RAG, and copilots inside your products—not science experiments

Yes, depending on constraints: private endpoints, VPC deployments, on-prem or hybrid patterns, strict key management, and data minimization in prompts. We document what leaves your boundary and how it is retained.

Grounding with retrieval, constrained tool use, citation requirements, eval sets from your domain, monitoring in production, and human escalation paths. Quality is treated as an ongoing process—not a one-off prompt tweak.

Caching, summarization, model routing, batching where possible, budgets per tenant or workflow, and dashboards for token usage. We design so finance and engineering can reason about spend the same way.

That is the common case. We integrate through APIs, events, and secure service accounts, respecting roles from your identity provider so copilots only see what the user is allowed to see.

A narrow workflow, clear acceptance tests, a small user group, and telemetry from day one. If the pilot meets thresholds, we expand; if not, we iterate or stop—without sunk-cost theater.

We agree a handover: runbooks, owners, change windows, and regression checks. Many clients keep us on retainer for model updates and eval refresh; others run fully in-house after training.

Use the contact form: describe the workflow, systems involved, data sensitivity, and desired outcome. We suggest a short discovery call and return with options—not a generic slide deck.