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Service 08

Artificial intelligence, applied where it actually pays.

We start with your processes, not with a model. Where AI creates measurable value we build it. Where it doesn't, we say so.

The offer

Most AI projects fail on the use case, not the technology

The models are capable and getting more so. The failures we see are almost always upstream: a use case nobody needed, data that was never ready, or a pilot with no definition of success.

Our work begins with an assessment of where your organisation actually spends effort — the repetitive reading, writing, searching, checking and routing that consumes hours without producing judgement. Those are the places AI pays.

We then prioritise by value and feasibility, build one use case properly end-to-end, measure it against agreed success criteria, and only scale what earns it. Deployment includes the parts people skip: data governance, privacy, residency, evaluation and the training that gets it adopted.

Capabilities

What we do

Delivered as an assessment, a build, or an ongoing partnership.

  • AI opportunity assessment across operations
  • Data readiness, quality and governance review
  • Use-case prioritisation by value and feasibility
  • Prototype and proof-of-concept development
  • Document processing, extraction and classification
  • Workflow automation and system integration
  • Internal knowledge assistants and search over your own data
  • Customer-facing conversational systems
  • Model selection, evaluation and benchmarking
  • Responsible-use, privacy and data-residency guidance
  • Monitoring, evaluation and drift management
  • Team training and adoption support

Packages

Assess, pilot, partner

Deliberately sequential. We would rather prove one use case than promise ten.

AI Readiness

Establishing where AI is worth applying before committing budget.

  • Process and data audit across key functions
  • Use-case long list scored by value and feasibility
  • Data quality and readiness assessment
  • Tooling and platform recommendation
  • Risk, privacy and governance review
  • Prioritised roadmap with indicative effort and cost
Request a quote

AI Partner

Organisations building an ongoing AI capability rather than a single tool.

  • Everything in Pilot Build
  • Multi-use-case delivery programme
  • Internal knowledge assistant across your own data
  • Ongoing model monitoring and evaluation
  • Responsible-use and data governance policy
  • Quarterly capability and roadmap review
  • Retained engineering and advisory support
Request a quote

Packages are a starting point, not a limit. If none of them matches what you need, tell us what does and we will scope it.

How it works

From first call to close-out

Assess

Where does effort go, what data exists, and which tasks are repetitive enough to be worth automating.

Prioritise

Use cases scored by value and feasibility, with success criteria defined before anyone builds.

Build

One use case taken end to end — integrated, evaluated, tested with real users and deployed.

Scale

Monitoring, governance and the next use cases, added only once the previous one has proven itself.

Questions

Before you ask

Does our data leave our systems?

Only if you decide it should. Data residency and privacy requirements are established at the assessment stage, and the architecture is designed around them — including fully self-hosted deployments where that is required.

We have no AI experience internally. Is that a problem?

No. Most of our clients start from zero. Training and adoption support are part of the Pilot Build package precisely because a tool nobody uses is a wasted investment.

What if the assessment concludes AI isn't worth it?

Then we tell you that, and the assessment has saved you a great deal more than it cost. In many cases the honest answer is that a process fix or a conventional automation delivers the same result more cheaply.

How do you measure whether a pilot worked?

Against criteria agreed in writing before development begins — typically time saved, error rate, throughput or cost per transaction, measured against a documented baseline.

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Talk to us about ai research & consultancy

Tell us the scope, the site and the date it has to happen by. We will come back with questions and a written proposal.

Make an enquiry