Intelligent agents that work within your business.
We engineer AI agents that take part in real business operations — with clear responsibilities, access only to what they are authorised to use, and people in control of the decisions that matter.
Why it matters
Work that moves without being chased
Agents prepare, route and follow up on routine work, so people spend their time on judgement rather than coordination.
Decisions with the full context
Agents gather the relevant records, documents and history before a decision is made — and show where each piece came from.
Automation you can trust
Every action is permitted, logged and reviewable. Where a process needs a human decision, the system waits for one.
Order confirmed for Thursday; ticket #1042 attached for the site team.
What we build
Specialised agents
Agents with a defined role — research, analysis, operations or execution — and the instructions, tools and limits that role needs.
Multi-agent orchestration
An orchestration layer that plans work, delegates tasks between agents and combines their results into one outcome.
Tool and system integrations
Connections to your APIs, databases and business applications, including Model Context Protocol (MCP) servers, scoped to what each agent may do.
Human-in-the-loop approvals
Approval steps placed where a process calls for judgement, with the context a reviewer needs to decide quickly.
Persistent tasks and workflows
Long-running work that survives restarts, tracks its own state and can be paused, resumed or handed to a person.
Assistants inside your applications
Agent capabilities embedded in the tools your teams already use, rather than another separate chat window.
How we build it
Map the process
We document how the work happens today, who decides what, and where an agent could genuinely help.
Define roles and permissions
Each agent gets a narrow responsibility, explicit tools and data access, and rules for when to ask a person.
Build and evaluate
We test agents against realistic scenarios and failure cases before they touch live systems.
Deploy with oversight
Agents go live with monitoring, audit logs and the ability to adjust their autonomy as confidence grows.
Safeguards
Least-privilege access
Agents only see data and use tools their role requires, under your existing identity and permission model.
Approval gates
Consequential actions require a person to approve them. Autonomy is configured per process, not assumed.
Full audit trail
Inputs, tool calls, decisions and outcomes are recorded so any action can be reviewed and explained.
Tested before release
Behaviour is checked against test suites before release, and we agree with you how it is reviewed once it is running.
Technical depth
We choose models and frameworks per project rather than by default, and design systems so that the model can be changed without rebuilding the workflow around it.
Common questions
Do agents act on their own?
Only where you decide they should. We design the level of autonomy for each process, and consequential actions can always require human approval.
Will this work with our existing systems?
Usually, yes. Agents work through your existing APIs, databases and applications. Part of discovery is confirming exactly what can be connected and how.
How do you stop agents making mistakes?
Narrow responsibilities, scoped permissions, evaluation against realistic cases before release, approval gates for important actions and monitoring once live.
Have a problem this could solve?
Tell us about it in your own words. We'll help work out whether this is the right approach — or whether something simpler would do.
