Services
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AI agents & LLM systems
Retrieval, tool-use and evaluation pipelines that run in production, on your data, with accuracy you can measure — not a chat box bolted on.
What we deliver
01
Use-case discovery
Find where a model actually beats a rule or a human, and define the success metric up front.
02
RAG & knowledge systems
Ingestion, chunking, embeddings, hybrid search and citation — grounded answers over your documents.
03
Agents & tool-use
Multi-step agents that call your APIs, with permissions, retries and human-in-the-loop checkpoints.
04
Evaluation harness
Golden datasets, automated evals and regression gates so every prompt or model change is measured.
05
Guardrails & safety
Input/output filtering, PII handling, rate limits and audit logging for regulated environments.
06
Deployment & cost control
Model routing, caching and observability — quality up, token spend down.
How it runs
We treat models like any other dependency: measured, versioned and replaceable.
01
Discovery & data
Pick the use case, gather the data, set the metric.
1–2 weeks
02
Prototype
Working pipeline against real data, first eval numbers.
2–3 weeks
03
Harden
Guardrails, evals in CI, latency and cost tuning.
3–6 weeks
04
Ship & improve
Production rollout, monitoring, monthly eval reviews.
Ongoing
STACK WE REACH FOR
ClaudeOpenAIOpen-weight modelsLangGraphpgvectorPineconePythonFastAPIVercel AI SDK
Model-agnostic. We route to the best model per task and keep you able to switch providers without a rewrite.
RELATED BUILD
AI research copilot
A grounded RAG copilot blueprint — a citation on every answer, evals in CI, and model routing to keep cost down.
Let's scope it.
A 30-minute call, then a written proposal with milestones and a fixed price. No retainers to get a quote.