Production-grade AI woven into your product — Claude-powered agents, copilots, and automation that ship reliably, not just demo well. Grounded in your data, measured by evals, and built to run every day.
Anyone can wire a chatbot to an API in an afternoon. The distance between that demo and a system your business can rely on — one that knows your data, uses your tools, stays within guardrails, and holds up under real usage — is engineering. That distance is what we cover.
We build AI features and autonomous agents on Claude and other frontier models: grounded in your knowledge through retrieval, connected to your systems through tools and MCP, tested against evaluation suites instead of vibes, and instrumented so you always know what the model did and why.
Autonomous agents and in-product copilots that plan, call tools, and complete multi-step work — with human-in-the-loop checkpoints where the stakes demand them.
Retrieval pipelines that ground the model in your documents, databases, and domain knowledge — so answers cite your truth, not the internet's.
Wiring models into your product and your stack — APIs, MCP servers that expose your tools and data, streaming UX, and prompt pipelines with caching that keeps costs sane.
Evaluation suites, guardrails, and observability — measured quality before launch, monitored behavior after, and cost & latency tuned for production economics.
We find the workflows where AI actually pays for itself — and tell you honestly where it doesn't. Feasibility first, hype never.
A working prototype on your real data within weeks, not quarters. The fastest way to learn whether an AI feature works is to put it in front of the people who'll use it.
Retrieval over your knowledge, tools into your systems, and prompts engineered against real cases — the model stops being generic and starts being yours.
An eval suite that scores quality on your actual tasks, plus guardrails for the failure modes that matter. If we can't measure it, we don't ship it.
Staged rollout, full observability over model behavior and spend, and continuous improvement as models — and your needs — evolve.
The gap between AI demos and AI products is real engineering: retrieval, evals, guardrails, observability. We've crossed that gap enough times to know exactly where it is — and how to get your product to the other side.