How is this different from RPA?

RPA follows a fixed script and breaks when the screen or the case changes. An agent plans against a goal, reads context, chooses tools and asks for help. In practice the two run together: deterministic steps stay scripted, judgement steps go to the agent.

What if the agent makes a costly mistake?

It should not be able to. Value thresholds, reversible actions and approval gates are configured before go-live, and the log lets you replay exactly what happened.

Government & Public Sector · Agentic back-office automation

Case-handling agents for public services

Agents that complete multi-step back-office work inside your systems.

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Straight-through case completionCycle time per caseException and escalation rateCost per transaction

The problem

What this fixes

Service-level commitments are published, but the work behind them is still manual routing, checking and chasing across departments and legacy systems.

Process-level automation where an agent plans, calls tools, reads and writes to enterprise systems and escalates when it is unsure, for reconciliation, onboarding, procurement, claims triage, and the long tail of exceptions that classic RPA scripts could never hold. Each agent has a defined scope, a permission set and an approval gate.

Government & Public Sector

Automated eligibility checks

Rules applied consistently, with every exception documented for review.

Government & Public Sector

Cross-department handoffs

Cases moved with their evidence instead of an email chain.

Government & Public Sector

SLA monitoring and chase

Cases approaching breach escalated automatically to the accountable owner.

Government & Public Sector

Full decision record

Every automated step logged in a form an oversight body can inspect.

In this sector

Where it earns its place

Delivery

How we build it

Foundation check

Before any build

We inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.

Narrow first release

First release

One workflow, one team, real traffic, measured against the baseline we captured before we started.

Controls and evaluation

Runs in parallel

Guardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.

Scale and hand over

After sign-off

Additional workflows, sites or languages, then runbooks and training so your team can operate it without us.

Data foundation

What has to be right first

A documented process with its real exception paths, not the idealised diagram.
APIs or stable interfaces for every system the agent must touch.
Service accounts with least-privilege scopes.
A ground-truth set of completed cases to evaluate against.

Controls

Guardrails and KPIs

A human owns every consequential decision

Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.

Every action is logged and replayable

Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.

Data stays where your regulator requires

In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.