AI in production, with the brakes fitted.

Retrieval over company data

Answers grounded in your own documents and records, with permissions respected and sources cited — not the model guessing.

Agentic workflows

Multi-step tasks the model can carry out across your tools, scoped to what it is allowed to touch and observable end to end.

Support & sales automation

Drafting, triage and follow-up handled by the model, escalating to a person the moment judgement is required.

Human approval gates

Anything with consequences waits for a person to approve it, so nothing irreversible is left to the model alone.

Evaluation & guardrails

Automated tests on real cases before launch, plus constraints on what the model can access, say or trigger in production.

Cost control

Smaller models for simple work, caching, per-task spend caps and token monitoring, so the bill is predictable and understood.

Enterprise AI integration is the work of wiring a language model to your data, your rules and someone's approval so it can do useful work safely. It is the plumbing and the brakes — retrieval, workflows, approval gates, evaluation and cost controls — not just a chatbot bolted on the side.

Yes. Most useful systems retrieve over your own documents, records and knowledge before the model answers, a pattern known as retrieval-augmented generation. We build the retrieval layer with permissions respected, so answers are grounded in your data and cite where they came from.

We put brakes on it. Anything with consequences passes through a human approval gate before it happens, and guardrails constrain what the model can access or trigger. Nothing irreversible is left to the model alone, and every action is logged so you can see what was done and why.

We are model-agnostic. We work with Claude, OpenAI and open-weight models, and choose per task on quality, latency, cost and data-handling terms. The architecture is kept portable so you are not locked to one provider and can switch as the market moves.

Cost is designed in, not discovered on the invoice. We route simple work to smaller models, cache and reuse where we can, cap spend per task, and monitor token usage so runaway costs are caught early. You get a predictable bill and a clear view of what each workflow costs to run.

Most integrations run eight to sixteen weeks from diagnostic to a live, handed-over system, depending on data readiness and how many workflows are in scope. We start with a short diagnostic so the scope, guardrails and evaluation criteria are agreed before any build begins.