AI agents that
do the work.
You keep the savings.

We don't build throwaway proofs of concept. Your first project is a production slice: one workflow, one system and one human approval point, built on production infrastructure with security, logging and monitoring from day one.

Before we start, we agree the success thresholds and a go/no-go date. If the numbers aren't there, we tell you — and sometimes the honest answer is that a simpler automation beats an agent.

We test before we trust:

• Evaluated on your cases, repeatedly Each agent is built against a golden test set drawn from your real work, including messy and hostile inputs. Every case runs several times, and we report how often the agent succeeds on every attempt — not just its best run — before launch and after every change.
• Grounded answers Responses cite your data. When confidence is low, a tool keeps failing or a request is policy-sensitive, the agent hands the full case to a person.
• Autonomy earned, not assumed Agents start in shadow mode — they draft, a person decides — then take a small share of live work. We extend their authority only when the evaluation evidence supports it.
• Traced, stoppable and reversible Every step can be replayed from its trace, any agent can be paused without touching your core systems, and a rollback returns you to the previous version or the manual process in minutes.
• Failures become tests We review traces weekly and add every real failure to the test suite, so the same bug can't ship twice.

• Your environment Agents run in your cloud, or in a UK/EU region you approve, using model endpoints configured so your data isn't used to train them.
• Least privilege Each agent has its own identity and only the access its task needs, with read and write access kept separate. Anything irreversible — payments, deletions, outbound messages — needs a person's approval, and every tool call is traced.
• Threat-modelled We design against the OWASP Top 10 for Agentic Applications.
• Paperwork done properly We sign a data processing agreement, support your DPIA, and design to the rules you work under — UK GDPR, HIPAA or your sector's regulator.

Yes — and that's most of the work. We build agents into the tools your teams already use: CRM, ERP, ticketing, document stores, EHRs, email and phone. We use open standards such as MCP and A2A rather than a proprietary platform, and because we're model-agnostic, we choose the model that fits your data, risk and cost — and you can switch later.

Each connection becomes a tool the agent calls, and we design those like public APIs — clear names, typed inputs and errors the agent can recover from — because that's where most agent failures start.

The diagnostic includes a data-readiness check, so integration surprises surface in week one, not month six.

First we measure your current cycle time, cost per transaction and error rate. Before any build, we set the agent's cost ceiling per task, its response-time target and its monthly run budget, so finance sees the numbers up front.

Work is fixed-fee by phase — a short diagnostic, then a production slice — each quoted before we start. After launch, a dashboard tracks savings, run costs and response times against your baseline. You own the code, prompts and evaluation suites.

Ask to see the engineering, not the slides. In a workflow review we'll show you:

• A working eval harness Real tasks with graded outcomes, and how we report consistency across repeated runs rather than a single accuracy number.
• A trace of an agent run Every prompt, tool call, argument, result and cost, step by step.
• The rollback plan The switch we use and how long recovery takes.
• Who owns it after launch Exactly what the handover includes — code, prompts, eval suites, traces and runbooks — and who you call when something breaks.

The engineers you meet are the ones who will build it.