AI built around a measurable task.

Agents scoped to a defined workflow, with agreed quality checks, access controls and a named owner for the result. The handover includes source code, evaluation tests and a breakdown of operating costs.

What every AI engagement holds to

  • One job per agent. Each agent is scoped to a task with a measurable outcome and built on a model selected against quality and cost thresholds.
  • Permissions in code. Authorisation is checked before every action, in code, never in a prompt. The system cannot approve its own changes.
  • Cost per outcome. Model choice, hosting and routing are decided on cost and latency, and the bill is reported alongside the result.

The offering in four parts

  • Task-scoped, cost-efficient agentsBuild

    Start with a defined task: triage a queue, draft a document or check a report. Agree on what a correct result looks like, test candidate models against representative inputs, and measure quality, latency and cost before rollout.

    • A defined task, inputs and acceptance criteria
    • Model comparison against representative examples
    • Evaluation tests included in the codebase
    • Source code and operating instructions for your team
    Deliverable

    A working agent with a cost baseline

    The handover includes the agent, its evaluation dataset, test results and estimated cost per completed task. Your team can rerun the checks when the model or workflow changes.

  • Workflow automation and agent coordinationCoordinate

    Connect the steps in your workflow with explicit inputs, outputs and ownership. Use ordinary automation for fixed rules and agents for tasks that need interpretation. Define where work stops for human review and how interrupted jobs resume.

    • Workflow map with hand-offs and escalation paths
    • Tool and data access scoped to each task
    • Human review before agreed consequential actions
    • Recovery procedures for interrupted work
    Deliverable

    A workflow your team can operate

    The delivery includes the integrations, a record of each run and an operating runbook. Recovery checks cover interrupted jobs, retries and duplicate requests before rollout.

  • Access controls tested before rolloutSecure

    Agree which data the application may read, which actions it may take and who can approve them. Implement these boundaries in application code, test permitted and denied requests, and record actions for review.

    • Documented access rules for users and integrations
    • Authorisation checks before protected actions
    • Tests for permitted and denied requests
    • Action logs with agreed retention and access rules
    Deliverable

    An access model with executable checks

    Your team receives the access rules, automated checks and logging configuration alongside the application. Changes to permissions can be reviewed and tested before release.

  • Evaluation and operating ownershipEvaluate

    Turn acceptance criteria into a repeatable evaluation: representative examples, expected results and release thresholds. Assign responsibility for reviewing output, investigating regressions and deciding when a model or workflow needs changing.

    • Evaluation datasets tied to the agreed task
    • Quality and cost thresholds for release decisions
    • Named review and escalation responsibilities
    • A reporting schedule and operating documentation
    Deliverable

    A repeatable release decision

    Each evaluation produces a report of quality, latency and cost against the agreed thresholds. The report and review checklist give your team the evidence to ship a change or send it back for revision.

Have a use case worth a real look?

Describe the workflow, the volume and who is accountable for the result. You get a written view on whether an agent pays back, what it would cost to run, and what has to be in place first.

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