AI implementation and adoption

What we do

• Find the real use-case. We audit your workflows and surface the handful of places where AI genuinely shifts the needle — not another chatbot.
• Ship production-grade integrations. Azure OpenAI, custom copilots, retrieval over your data, vision + document extraction, evaluations and guardrails.
• Build agentic workflows. MCP servers on top of your existing APIs so AI agents can safely read, act, and close loops inside your systems.
• Measure what matters. Eval harnesses, drift monitoring, cost dashboards — the boring scaffolding that keeps AI from regressing in production.
• Train your team to own it. We hand the keys over at the end. You shouldn't need us for every prompt tweak.

What we build with AI

Concrete things we help customers ship — a rolling inventory of the shapes a problem can take.

How an engagement runs

1. 01 Two-week discovery — we pick the one use-case with the highest leverage for your business, not the easiest to demo.
2. 02 Prototype → measured pilot — we ship a thin slice into real hands, then instrument and iterate on it.
3. 03 Production rollout — we harden the pipeline, wire observability, and put sensible guardrails around the model.
4. 04 Enablement — your people learn the stack, the evals, and the playbook. Then we step aside.

In practice

Four quick sketches across different sectors — not past customers, shapes of engagement. Concrete enough to argue with.