AI copilots
LLM features built with a production-systems mindset — streaming, tool use, eval harnesses, and honest scoping about what the model actually solves.
LLMs are useful for the right problems. Before writing a line of code, I scope honestly — if a regex solves it, we use a regex. When an LLM does add value, the integration is built for production: streaming responses, tool use, structured outputs with validation, an eval harness to catch regressions, and prompt management you can tune without a redeployment. I work with Claude and OpenAI APIs and can build RAG pipelines over your documentation, tickets, or product data.
What's included
- Claude / OpenAI integrations with streaming, tool use, and safe defaults
- RAG over your docs, tickets, or product data
- Eval harnesses so the assistant doesn't regress quietly
- Honest scoping — you'll know when 'AI' is actually solving the problem
Related work
Got a project that's been waiting too long?
We respond to every inquiry within one business day. No funnels — just a real conversation about whether we're a fit.