Part of Production MCP Servers & Agentic Workflows
Production AI Systems
Applied AI taken all the way to production: retrieval over your data, model integration behind a provider-agnostic interface, and the evaluation that proves it works.
The model is rarely the hard part. Choosing a problem worth automating, wiring it to real data and measuring whether the output can be trusted is where AI projects succeed or stall. We start there and ship systems with measured accuracy, latency and cost.
What that includes
- Opportunity assessment
- A short written review of where AI pays for itself in your business, with operating costs attached.
- RAG and knowledge systems
- Retrieval over your documents and data with citations and evaluation from day one.
- Model integration
- Claude, OpenAI, Gemini and open-weight models behind one interface you are not locked into.
- Evaluation and guardrails
- Test sets, regression tracking, cost ceilings, PII handling and human review paths.
What you end up with
- A recommendation you could put in front of a board.
- Systems in production with measured accuracy, latency and cost.
- No dependence on a single model vendor.
Selected work
Case study: RentServers.com
Shipped from zero to a production clearinghouse in under 14 days, normalizing multi-vendor bare-metal and GPU quotes with server-rendered pages delivered in under 40 ms and native MCP agent discovery.
Read the case study