Service line · AI
Applied AI engineering
I add AI to real products: LLM features and retrieval over your documents, agents that act in your tools with permissions and audit logs, and the evaluations and guardrails that keep them honest and within budget. Every engagement starts small, with a one-week scoping sprint on your real data.
Sound familiar?
- You want an AI feature, not a demo that falls apart on real data.
- Your team wants agents that can act inside internal tools, and you need them to be safe.
- Your LLM bill is growing, and nobody can say whether the answers are getting better or worse.
Ways to work together
- Fixed-price first step
AI features in your product
LLM features that work on your real data, measured before they ship.A scoping sprint first, then a timeline agreed per project - fixed-scope project
AI agents and MCP servers
Agents that act in your tools with permissions, audit logs and a person in the loop.Agreed per project after a scoping call - fixed-price
LLM evals, guardrails and cost control
Know whether your AI feature is getting better or worse, and what it costs.Agreed per project after a scoping call
Questions
Do you train models?
I integrate and deploy models rather than train foundation models from scratch. That covers using hosted models, running open-weight models on your own infrastructure, and building the retrieval, evaluation and safety layers around them.
Will our data be used to train someone else's model?
Not if we choose providers and settings that prevent it. Where data can't leave your infrastructure at all, the model runs inside it.
Tell me what you’re building.
Tell me about the problem. I’ll reply with the smallest useful first step and when I could start.