AI
AI agents and LLM apps
Most AI projects stall between the demo and the thing your team can rely on every day. We build the part after the demo, with evaluation, guardrails, monitoring and a clear handover.
What you get
- Agents that read, decide and act inside your existing tools
- Retrieval over your documents, tickets and data with citations
- LLM features inside your product, from drafting to classification
- Workflow automation with a human approval step where it matters
- Evaluation suites so you know when a model change breaks something
- Cost and latency budgets, with routing between models
How we do it
We start with a workflow review: what the task is, where decisions are made, what the exceptions look like and which data is involved. Reading and answering usually means retrieval. Deciding and acting means an agent. You get that read on the first call.
Then we build the smallest version that does real work, put it in front of two or three of your people, and iterate weekly until it is trusted. Only then do we harden it: evaluation sets, rate limits, cost controls, monitoring and runbooks.
Questions about this
Will you train on or expose our data?
No. Your data stays in your accounts. We use providers with zero-retention terms and can deploy inside your cloud.
What happens when the model is wrong?
Every agent we ship has a fallback path, a confidence threshold and a log you can audit. Low-confidence actions go to a person.