AI & Automation

Make intelligent technology part of the workflow.

AI is worth using where it removes a specific, measurable cost — not where it makes a good demo. We start from your workflow, find the steps where judgment is repetitive and evidence is available, and build something with a human in the loop and an evaluation you can trust.

Discuss this service

Is this you?

Signs you need this.

Skilled people spend hours a week re-keying data between systems.

Documents arrive as PDFs and leave as spreadsheets, by hand.

Response times are limited by how long triage takes, not the work itself.

You've run an AI pilot that impressed everyone and shipped to nobody.

You need AI in a regulated process and don't know how to evidence it.

What you get

The deliverables.

Automation opportunity map

Every candidate step scored by volume, time cost, error rate and how tolerant it is of being wrong.

Feasibility spike

A narrow, honest test on your real data before anyone commits to a build.

Evaluation harness

A test set and scoring method, so quality is a measurement rather than an impression.

Production workflow

The automation running inside the process, with human review where the stakes require it.

Guardrails and audit trail

Access control, data handling, prompt and model versioning, and a record of every automated decision.

Monitoring and drift plan

Dashboards on accuracy, cost and volume, plus what to do when quality moves.

How we work

The approach.

Map the workflow

We watch the process as performed, then mark the steps that are repetitive, evidence-based and high volume.

Test on real data

A time-boxed spike against your actual documents and records. Sometimes the answer is that rules beat a model — we'll say so.

Build the loop

Human review, confidence thresholds and clean escalation paths designed in from the start.

Measure and expand

Prove the saving on one step, then extend to the next with the same evaluation harness.

Logistics

How we engage.

Team Shape

AI lead, data engineer, ML/backend engineer, service designer

Starts With

A workflow mapping session and a feasibility spike on real data

Capabilities

Document understanding and extractionRetrieval-augmented assistantsClassification and triageWorkflow and process automationSystem-to-system integrationEvaluation and quality measurementAI governance and audit trailsHuman-in-the-loop design

Relevant Industries

Life Sciences Healthcare Real Estate

Common Questions

What clients ask.

Does our data get used to train models?

Not unless you ask for it. We default to configurations with no training on your data, and the data-handling terms are agreed in writing before the first spike.

What if AI turns out to be the wrong tool?

Then we don't use it. A rules engine or a fixed integration is often cheaper and more reliable, and the spike exists partly to reach that answer early.

Can this work in a regulated process?

Yes, with human review, versioning and a complete audit trail. We design for the evidence your auditor will ask for, not just the outcome.

Ready to start on AI & Automation?

Let's discuss how this fits into your current challenges and what the first step looks like.

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