Practical AI consulting for teams that want useful systems, not a folder full of prompts. For UK teams, the value is in making the work specific: where the data comes from, who reviews it and what the first useful version should do.
Nationwide projects need consistency across teams, systems and regions. The aim is a way of working that does not depend on one person remembering every step. Wellingborough based, remote first, travelling only when on-site work actually moves things forward.
Mixed UK client base spanning home improvement, specialist trades, insurance, e-commerce and professional services. Documented programmatic SEO, technical migration and AI workflow work from 2015 onwards through SEO Moves.
Most national clients are remote first by default. Work runs on async documentation, weekly active-sprint calls and quarterly review days. Travel reserved for migration kickoffs and workshops where in-person changes the outcome.
Async by default. Weekly video during active sprints, fortnightly otherwise. On-site only where it materially changes the outcome.
The national consultancy market splits between large agencies and single-discipline specialists. Both have a place. My slot is senior individual-contributor time without the agency cost layer.
I help map the work, choose the first sensible use case, build the workflow and leave the team with clear rules.
The work is not a bundle of prompts. It is a decision about where AI belongs, what information it can use, who checks the output and how the team keeps the system tidy after the first version.
The patterns I see most before the work starts. If two of these sound familiar, the first review call is usually worth the time.
The team can see potential, but nobody has narrowed it into one useful workflow with clear boundaries.
Outputs change because the inputs, examples and review rules are not documented well enough.
AI sits between marketing, operations and leadership, so decisions slow down or become tool-led.
The business needs source checks, human review and sensible limits before AI can become useful.
I help choose the AI project that is worth doing first, then define what the system should and should not do.
That means mapping the work, setting source rules, building the first workflow and keeping human review where it matters.
The result is a controlled AI process your team can use without guessing the method each time.
Start with the repeated work, the risk level and the people who need to trust the output.
Define approved inputs, review points, source checks and what the workflow must avoid.
Prompts, examples, handoffs and checks joined into one usable operating model.
Handover notes and review habits so the system can improve without becoming loose.
Two examples of the shape this work takes. Real engagements, anonymised. Not every project looks like these, but the discipline is the same.
The team wanted AI support, but every idea was too broad. The first useful project became a research workflow with approved sources, summary rules and human review before anything reached a brief.
// outcome The team gets a repeatable research pack instead of a loose chat history.
Leadership needed AI use to move without creating risk. The work became a practical operating guide: what AI can draft, what it cannot touch and where people must check the output.
// outcome AI becomes usable because the boundaries are clear.
Book a review call. We will look at the work, the risk and the first sensible step before agreeing anything.
Book a review