AI you can put in front of an auditor.

The offering in four parts

• Task-scoped, cost-efficient agentsBuild

  An agent earns its place by doing one job well: triaging a queue, drafting a first version, checking a report. Each one is scoped tightly, built on the smallest model that does the job reliably, and measured on cost per outcome. One production system built this way runs several models side by side and routes each task to the cheapest one that meets the bar, so the bill stays predictable as usage grows.

  • Task-scoped agents, not general-purpose assistants
  • Model selection driven by cost and latency
  • Evaluation built in from the first version
  • Handed over as a codebase your team can read and extend

  In production

  Run on hardware that was already paid for

  One production agent fleet runs on infrastructure that was sitting unused rather than on new cloud spend, at more than 10x lower cost than renting the equivalent compute, month after month. Cost was an engineering constraint from the first commit.

• Agent orchestration (AMOS)Coordinate

  The hard part starts once there is more than one agent. AMOS, the Agent Management Orchestration System, is a control plane that routes work between agents, gives each one the tools and context it needs and nothing more, and keeps a human in the loop where the stakes call for it. One deployment coordinates more than 300 purpose-built routes across a large regulated organisation.

  • Routing, hand-offs and escalation paths
  • Scoped tool and data access per agent
  • Human-in-the-loop checkpoints where the stakes call for it
  • A record of what every agent did and why

  In production

  Zero records lost in a live shutdown test

  A core service was deliberately stopped in the middle of live production traffic to prove the recovery path holds. Zero data was lost, and the people using the system at the time did not notice.

• Permission checks in code, not in promptsSecure

  Controls are designed before capabilities. Every action an agent takes passes an authorisation check enforced in code, every read is checked live against the actual person asking rather than a shared bot identity, and the system cannot approve its own changes. When the question comes later, what happened and why, there is a log, not a guess.

  • Default-deny authorisation on every tool call
  • Full audit trail of agent actions and decisions
  • Per-requester data access across every integration
  • Close to 1,000 automated tests on the permission layer

  In production

  The agent never sees more than the person asking may

  In production, each read is checked against the requester's own access, so a session cannot surface anything that person is not already entitled to. Close to 1,000 automated tests cover that enforcement layer.

• Governance that survives an auditGovern

  Adoption that holds up when an auditor asks how a decision was made: evaluation that catches regressions before users do, governance mapped to how the organisation actually decides, and documentation that answers "show me". A system built under this discipline operates today inside a regulated organisation's own risk and compliance functions.

  • Evaluation frameworks tied to business outcomes
  • Governance mapped to existing decision-making
  • Regulatory fit reviewed with your compliance team
  • Performance reported as measured, including the weak weeks

  In production

  The real number, published on a schedule

  One system reports its own measured performance publicly on a fixed schedule, including the weeks the figure is not flattering. That is what governance buys: a number a regulator can ask about and get a straight answer to.