Adam MillsIT & AI Consultant

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AI Development

AI holds immense potential, but unlocking it takes precision and expertise. I help you find the right use cases, from intelligent automation to generative AI, and build solutions tailored to your business that deliver measurable impact.

I have been building with AI in production since before the hype, and I remain sceptical of the hype. The useful work is contextual: models that see your data, your rules and your documents, wired into the systems your people already use, with a person in the loop where it matters.

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What it covers

What AI Development includes

Scoped to what you need, from a single piece of advice to the whole programme.

  1. Use-case discovery

    Finding the bounded, measurable problems where AI earns its place, and being honest about the ones where it does not.

  2. Visual AI and classification

    Models that assess photographs and documents: what is in the picture, which category it belongs to, and whether it meets the rule.

  3. Generative AI and assistants

    Assistants and copilots built into your portal, case management or contact centre, drafting, summarising and answering within the rules you set.

  4. Knowledge bases and retrieval (RAG)

    Retrieval-augmented generation over your policies, manuals, case files and contracts, so an assistant answers from your own documents, cites the source and says when it does not know.

  5. Intelligent automation

    AI decisions embedded in workflows, with confidence thresholds, human review of the edge cases and a full record of every decision.

  6. Agentic workflows

    Agents that take bounded actions in your systems, raising a case, updating a record or requesting a missing document, within the permissions and limits you set, and with every action logged.

  7. Private and sovereign deployment

    Models hosted in your own tenancy or on UK-resident infrastructure, so patient, citizen and customer data never leaves your governance boundary.

  8. Speech and conversational AI

    Transcription, summarisation and redaction of calls and meetings, and voice or chat front ends for contact centres that hand over to a person when they should.

  9. Predictive analytics

    Forecasting demand, risk or churn from your structured data with well-understood machine learning, for the problems that never needed a language model.

How I deliver it

Four habits I bring to every engagement

The difference between a demo and a deployment is everything that happens after the demo.

  1. Find the case

    A bounded problem with a measurable outcome and enough real data to prove it. If there is no such case yet, I say so.

  2. Prove it

    A prototype on your real data, with accuracy measured against people doing the same job, before anything is committed.

  3. Build it in

    Integrated with your case management, portal or workflow, with human oversight and an audit trail designed in from the start.

  4. Monitor

    Accuracy, drift, cost and the decisions overturned by people, reviewed on a schedule and reported plainly.

Where it applies

Sectors where this matters most

  • Local governmentCitizen-facing services where documents and photographs arrive in every shape and quality, and no team has the hours to check each one.
  • Insurance and financial servicesClaims, onboarding and case triage where an AI decision has to be explained and, when challenged, defended.
  • HealthcareReferrals and records handled within information governance, with the clinician in the loop.

Proof

Where I have done it

Further reading

Other ways I can help

The other seven services

Your use case

Have a use case, or a suspicion there is one?

Bring the problem and a sample of the data. A short proof of concept will tell you whether it works before you spend on it.

Start a conversation Call 07868 158868