AI agents that do real casework.
We design and build agents that take on bounded, multi-step work (reading a submission, checking it against policy, calling the systems involved, drafting the decision) and hand it to a person whenever the stakes say so.
Most agent demos never reach a case file.
An agent that answers questions in a sandbox is easy. An agent that touches a licence renewal, an insurance claim or a supplier invoice has to work with incomplete documents, legacy systems, approval rules nobody wrote down, and an auditor who will ask why it did what it did.
That is where most agent projects stall. The model is rarely the problem. What's missing is scoped access to the right systems, a clear line between what the agent decides and what a person decides, and a record of every step that someone can replay later.
We start from the casework itself: who does it today, what they check, where they hesitate, and which steps are safe to automate. The agent is then built around that, not the other way round.
What we build and run for you.
Single-task agents
One job done well: triage an inbox, extract and validate a submission, reconcile a record. Fast to prove, easy to measure.
Multi-agent workflows
Intake, policy and action agents coordinated by an orchestrator you can inspect, mirroring how your teams already split the work.
Tool and system access
Scoped connectors into ERP, CRM, registries and case systems, read-only by default, with write access granted per action.
Agent operations
Run logs, replay, evaluation dashboards, cost tracking and on-call ownership once the agent is live.
Production, not pilots. Built to run inside your operations.
Every agent we ship has an owner, an evaluation set and an audit trail before it touches a live case.
From inbox to decision, with a person where it matters.
Perceive
intakeThe agent reads what arrives (forms, PDFs, emails, photos) and turns it into structured fields with confidence scores.
Reason
policyIt retrieves the rules that apply and checks the case against them, citing each clause it relies on.
Act
toolsIt calls the systems it is permitted to use, gathers what is missing and prepares the outcome.
Ask
human gateAbove set thresholds of value, risk or uncertainty, it routes the decision to a named person with the evidence assembled.
- Permissions scoped per tool and per action
- Human approval thresholds you set and can change
- Every step logged and replayable
- Evaluation set agreed with your team before go-live
- Fallback to a person when confidence drops
Where it earns its keep.
Illustrative patterns from the sectors we work in. Client details stay anonymised.
Licence and permit renewals
Read the application pack, check registry records and policy, flag exceptions, and draft the decision for an officer to approve.
Claims and KYC triage
Assemble the evidence, score risk, settle routine cases within limits and escalate the rest with a written rationale.
Invoice and contract matching
Three-way match invoices, explain variances against contract terms and post to the ERP with an audit note.
Service desk resolution
Classify requests, check entitlement, fulfil standard changes and hand complex tickets to an engineer with context attached.
Chosen for the job, not the vendor.
Work this builds on.
A regulator's entire licensing lifecycle, rebuilt as one digital platform
One trading network for over a thousand partners, monitored end to end
Client details anonymised per delivery agreements.
Questions we get asked.
Is agentic AI ready for enterprise work?
For bounded, well-instrumented workloads, yes. For autonomous decision-making with consequential output, no — not yet, and probably not in the architecture you would deploy today. The current useful pattern is structured multi-step agents with explicit tool boundaries, function-calling, retry and idempotency baked in, full audit trail, and a human gate before anything mutates a system of record. That works. "Set the agent loose on the estate" does not.
Can you actually ship AI into production, or is this another pilot factory?
Production is the bar. Most AI engagements that go badly end at a demo because the team treated retrieval quality, guardrails, evaluation harness, and human-in-the-loop as optional. We treat them as the minimum viable product. If a programme cannot define what production looks like — what "good" means, who reviews the output, how regression is caught — we will say so before contract.
What do you need from us to start an AI programme?
A real use case, an honest data inventory, and a stakeholder who can answer "what would good look like" with specifics. The use case does not have to be glamorous — "reduce field-inspection report turnaround from four days to four hours" is more useful than "add AI to our platform." We will not start a programme without those three, because the failure mode of AI projects that lack them is well documented.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.