Automate manual work. Keep people in control.

We automate repetitive reading, matching, data entry, drafting, sending, and status updates. Decisions that require judgement remain with the right person.

Audit one workflow
The same freight intake: four hours by hand, 18 minutes automated.Each block shows the time spent on one step.
By hand4h
Automated18m
Automated, enlarged18m

61% of the remaining time is the orange block — a person deciding something.

Automation should remove repetitive handling, not accountability. We define approvals, exceptions, and escalation before an agent is allowed to act.

What we audit, automate, and operate.

Start with an audit, a specific workflow build, or support for an existing automation. The scope expands only when the measured result justifies it.

  1. Workflow audit and automation plan

    We follow how work moves today, where time is lost, which systems and data are involved, and what should stay a human decision. You get a prioritised plan with a costed first project.

    • Process mapping
    • Time and cost baseline
    • Roadmap

  2. Workflow automation and system integration

    The rules, integrations, states, retries, approvals, and exception queues that move work between email, documents, CRM, ERP, databases, and internal software.

    • APIs and events
    • Workflow logic
    • Integration

  3. MCP servers and agent tool access

    Model Context Protocol servers that expose approved tools, data, and actions to AI clients. Authentication, scopes, permissions, schemas, auditability, and failure handling are part of the build, not a later hardening pass.

    • MCP servers
    • Client integration
    • Access and audit

  4. Custom AI agents

    Agents that read documents, find context, classify requests, compare information, draft outputs, and take approved actions in your systems — with their scope and permissions written down.

    • Documents and data
    • Tool use
    • Defined permissions

  5. Business knowledge and agent training

    We organise the material an agent needs, build test cases out of real work, define what a correct output looks like, and close the feedback loop so it improves without drifting.

    • Knowledge prep
    • Test cases
    • Feedback loops

  6. Human review, permissions, and audit trails

    Approval gates, confidence thresholds, escalation paths, permissions, overrides, and a record of what the system did and why it did it.

    • Approvals
    • Permissions
    • Audit trails

  7. Monitoring, support, and improvement

    We watch quality, failures, speed, and cost after launch, train the people who own the workflow, and update the automation as your process, software, and models change.

    • Monitoring
    • Team training
    • Improvement

Where automation creates measurable value.

The strongest candidates are repetitive, cross several systems, consume meaningful staff time, and have exceptions that can be clearly routed to people.

Logistics

Extract shipment data and prepare bookings.

Shipment intake, document extraction, validation, booking drafts, status updates, exception queues.

Finance and construction

Process invoices and route exceptions.

Invoice matching, missing-receipt follow-up, approvals, project routing, reconciliation, reporting.

Customer operations

Classify, draft, escalate.

Intake, classification, grounded replies, QA, escalation, resolution tracking.

Healthcare and regulated work

Prepare cases and keep decisions reviewable.

Referral intake, evidence readiness, policy lookup, scheduling preparation, citations, human review.

SaaS and professional teams

Make internal knowledge usable.

Internal search, drafting, onboarding, research, account preparation, multilingual support.

Commerce and distribution

Clean product data and publish it.

Supplier onboarding, catalog normalisation, enrichment, channel publishing, returns, fulfillment handoffs.

Keep the workflow independent from one AI provider.

Your business rules, knowledge, permissions, tests, and monitoring stay outside the model. That makes a provider change a controlled technical update instead of a workflow rebuild.

KnowledgeControlsempty bayQualityYoursModelswapped, not rebuilt
Knowledge
The policies, examples, terminology, and data an AI system needs to do useful work.
Controls
Which actions run automatically, which need approval, and who can override the system.
Quality
Output quality, failures, speed, and cost, measured so the system improves from evidence.
Model
Kept behind a clear interface, so a new provider is a swap rather than a rebuild.

Results measured against the previous process.

All case studies
4h → 18mShipment intake timeInternational freight+2.3×Invoices handled per analystConstruction finance−41%Time to first claim reviewInsurance claims

Start with one high-value workflow.

We start with a process that has visible cost and enough volume to matter. The first goal is a working, measurable automation — not a transformation programme.

01AuditWeeks 1–2

Measure the current workflow and its exceptions.

We trace the work across every tool and person it touches, write down the exceptions nobody documents, and measure volume, time, and the cost of getting it wrong.

You receiveA measured baseline and costed first build

02BuildWeeks 3–8

Build and test against real systems and cases.

We connect the actual systems rather than a sandbox, run representative cases through them, add the controls the process requires, and test with the team that will own it.

You receiveA tested workflow, with its exceptions handled

03RunOngoing

Launch, train owners, and expand only when results justify it.

We launch it, train the owners, monitor quality and cost in production, and expand the scope only after the agreed measures improve.

You receiveA system your team can operate

Which process is wasting the most time?

Describe the repetitive steps, the systems involved, and where errors or delays occur. That is enough for us to begin a workflow audit.

Audit one workflow Or talk directly