The problem
Most AI projects die as demos. A flashy prototype, a press post, then nothing — because it was never wired into the work people actually do. We start from the workflow, not the model.
What we build
Claude, GPT, or open-source models wired into your stack: retrieval over your own data, agents that take real actions through tools, and the guardrails that keep it predictable. Embedded where the work happens — not as a chatbot bolted on the side.
What's included
- RAG & memory over your documents and data
- Agent & tool-use architectures that take action
- Model-agnostic design — swap models without a rewrite
- Evals & guardrails for predictable output
- Full handoff — you own the code and the stack
How we work
Four steps, no theatre. Listen to the real problem, map the architecture and integration points, build working code you see in week one, then hand off full knowledge so you own it. Typically 4 to 12 weeks.
Related
Want AI that stays on your own infrastructure? See local LLMs. Or read how we built StrikeAhead — bite-window prediction from real signals.