Turning AI into systems that ship
The hard part of AI is rarely the demo — it is what comes after: running it in production, and being able to trace what went wrong. That is the part we do.
What we do
LLM integration, RAG retrieval, turning messy data into usable structure, and agent-style workflows. Calling an API is easy; the hard part is holding up under real data and real traffic.
Hallucination control & data validation
Models say wrong things, so we never let their output go straight downstream. A validation layer sits in between: rule checks, comparison against known data, and flags on low-confidence results. Errors should be blocked, and traceable.
What we think about first
Cost, latency, whether data leaves your environment, and whether you can observe it afterwards. Some steps are better with a model, some are worse — that needs sorting out up front.
Where it fits
Internal knowledge retrieval, automating repetitive judgement or clean-up steps, or wiring AI into an existing system rather than rebuilding the whole thing.