AI & Intelligent Automation

Most organizations have now seen an AI prototype work. The hard part arrives afterward: the model answers confidently from the wrong document, costs climb without anyone noticing, and nobody can say whether last week's prompt change made quality better or worse.

The gap is rarely the model. It is the absence of everything around it - retrieval that returns the right context, guardrails for actions with real consequences, a test set that catches regressions, and a human review path for the cases that genuinely need judgment.

We start by defining what a correct answer looks like for your use case, and turn that into a written evaluation set before any interface gets designed. That set becomes the thing we optimize against, so improvements are measured rather than claimed.

From there we build the retrieval layer, tool boundaries, and orchestration your workflow actually needs - keeping expensive model calls where they add value and ordinary code everywhere else. Every action that changes data, sends a message, or spends money passes through an explicit approval boundary you control.

Monitoring ships with the first release: request traces, token spend, latency, and quality sampling, so you can see how the system behaves in production rather than inferring it from complaints.