A strategy nobody can build
A polished list of opportunities with no view on data, cost or effort — so it never turns into a project anyone can scope, price or deliver.
AI consulting · UK
AI Consulting for UK Businesses
Practical AI strategy for leadership teams who need decisions, not a slide deck: which use cases are worth pursuing, whether your data and people are ready, what to build, buy or leave alone — and a working prototype that tests the plan before you commit a budget.
- Vendor-neutral build, buy or partner advice
- Strategy tested with a working prototype
- Governance support for your DPO and board
Why consult first
Where AI plans go wrong before any code is written.
The expensive AI mistakes are usually made in the planning: the wrong use case, an untested assumption, or a policy gap that staff have already filled for themselves.
What good AI consulting leaves behind
Not a deck for the shelf, but things you can act on the following week:
- A ranked shortlist of use cases, with baselines
- A build, buy or partner call for each one
- A tested prototype of the strongest idea
- A staff AI policy and a governance plan
- A costed roadmap your board can approve
Staff already using AI, unofficially
People pasting client emails and contract clauses into free chatbots, because nobody has said which tools are allowed or with what data.
Every department wants a pilot
More ideas than budget, and no fair way to compare the finance team’s idea with customer service’s — so the loudest one wins.
Vendors marking their own homework
Each supplier recommends the product it sells. Nobody compares building, buying and doing nothing on the same terms.
What you get
Documents you can use, and a prototype that works.
A consulting engagement ends with written outputs your team keeps and a prototype running on your own data — so the recommendations have been tested, not just argued.
Use-case shortlist
Ideas gathered from across the business and scored on value, feasibility, risk and readiness, with the reasoning written down so the ranking can be challenged.
AI readiness assessment
An honest look at your data, processes, people and systems: what is ready now, what needs work first, and what that work involves.
Our AI consulting method
Build, buy or partner calls
For each shortlisted use case: whether an existing product covers it, a specialist vendor fits better, or a custom build is justified.
Baselines and business case
How each process performs now, what success would look like in your own measures, and the full cost of getting there and running it.
Governance pack
A draft staff AI policy, a human-oversight design, questions for AI vendors and technical inputs for your DPIA — ready for legal and HR review.
Working prototype
The strongest use case built as a prototype on a sample of your real data, and tried by the people who would use it.
What we can build afterwards
Independent-minded advice
Advice from people who build — without the sales pitch.
We build AI software as well as advise on it, and you should weigh our advice with that in mind. It is also why the advice tends to be practical: we know what a use case costs to build, what it costs to run and where projects like it get stuck, because that is our day job.
We keep the two apart in how we work. Consulting is a fixed fee, agreed before we start. Recommendations come with their reasoning in writing, including the ones that say “buy this product” or “not yet”. And the roadmap, prototype and documents are yours to take to any supplier, or to deliver in-house.
We are not tied to any AI vendor. Often the right first move is a feature already inside software you pay for — your CRM, helpdesk or office suite — or a no-code tool set up properly. Custom builds are for the use cases where your process, your data or your product genuinely needs one.
- A fixed consulting fee, agreed up front
- Written reasons for every recommendation
- “Buy” and “not yet” are legitimate answers
- Outputs you can take to any supplier
AI readiness
What we look at before recommending anything.
Readiness is not a score. It is a list of specific things that must be true for a use case to work — and the effort needed where they are not.
Can the AI reach what it needs?
Where the knowledge lives — documents, the CRM, shared drives, people’s heads — how current it is, and whether access can respect existing permissions.
Is the work stable and repeatable?
A process that changes weekly, or rests on judgement calls nobody can describe, is a poor first candidate however exciting it sounds.
Do people want it, and can they use it?
Adoption fails when the people doing the work were not involved. We talk to them directly, and look at the skills and training a change would need.
Can your tools be connected?
APIs, data exports and access rights for the systems involved. An idea that depends on data locked inside an old system carries hidden costs.
Who signs off, on what evidence?
Your DPO, IT security and, in regulated sectors, compliance. Knowing what they need to see stops a good pilot stalling at approval.
Is it worth the running cost?
Model fees, hosting, review time and support set against the time or revenue at stake. Some ideas work technically and still do not pay.
Risk and governance
AI governance that helps people say yes safely.
Good AI governance is mostly clarity. Staff need to know which tools they may use and with what data; managers need to know who approves a new use; and your board needs to see that risks are tracked by someone accountable. Without that, people either avoid AI entirely or use it quietly with no controls at all.
We draft a short, readable AI use policy for staff, design human oversight into each shortlisted use case — who reviews, when a person must decide, how mistakes are caught — and prepare the questions to put to any AI vendor about data retention, model training and processing location. For each use case involving personal data, we map the data flows your DPO needs, following the ICO’s guidance on AI and data protection.
If you are regulated — by the FCA, the SRA or a health regulator, for example — we shape the evidence around what your compliance team expects to see. If you sell into the EU, the EU AI Act may apply to you as well. We are not lawyers: policies and assessments go to your legal, HR and data protection advisers for sign-off.
- A plain-English AI use policy for staff
- Human oversight designed into each use case
- Vendor questions on data retention and training
- DPIA inputs and data-flow maps for your DPO
- A simple risk register with named owners
An example roadmap
What a first-year AI roadmap can look like.
The roadmap is the main output of an engagement. This is the shape we usually recommend: policy and safe quick wins first, one measured pilot next, shared foundations only once several use cases need them.
An illustration of how recommendations are sequenced, not a template or a promise: your roadmap is built from your own shortlist, budget, capacity and appetite for risk.
- First month
Policy and safe quick wins
Publish the staff AI use policy, switch on AI features in tools you already pay for where they are safe to use, and show people how to use both well.
Sanctioned, controlled use of AI
- Months 2–3
The first pilot
Build the top-ranked use case as a pilot on one team’s real work, with a person reviewing output and the baseline ready for comparison.
Measured pilot results
- Months 4–6
Scale, rework or stop
Scale the pilot if it beat the baseline at an acceptable cost; rework or stop it if not. Start the second use case either way.
One use case in daily operation
- Months 6–12
Shared foundations
Once two or three use cases are live, shared components for retrieval, monitoring and access control, so later use cases do not rebuild the same plumbing.
Reusable AI foundations
- Each quarter
Review and re-rank
Models, prices and priorities change. A short quarterly review re-ranks the backlog and retires anything that has stopped earning its keep.
A roadmap that stays current
How we work
From first workshop to a tested roadmap.
The same seven stages we use to deliver software, applied to a consulting engagement — which is why the plan at the end has already met real data and real users.
- 01
Discovery
Interviews with leadership and front-line teams, and a look at the systems and data involved. We gather candidate use cases from across the business and note what is already happening unofficially.
A long list of candidate use cases
- 02
Strategy
Each use case is scored on value, feasibility, risk and readiness, and compared across building, buying, partnering or waiting. The strongest go forward with a measured baseline.
A ranked shortlist and build-or-buy calls
- 03
UX & architecture
For the top use case we design how people would work alongside the AI — what they see, what they approve, when it hands over to them — and the data flow underneath.
Prototype design and data-flow map
- 04
Development
A working prototype on a sample of your real data, built in a short sprint, so the recommendation rests on evidence rather than a vendor’s claims.
A prototype your team can try
- 05
Testing
The people who would use it try the prototype, and we measure it against the baseline: accuracy, handling time, cost per task and the cases it gets wrong.
Test results you can share
- 06
Launch
We present the roadmap, business case and governance pack to your leadership team, with the prototype running so the discussion is about something real.
A roadmap and a decision
- 07
Optimisation & support
Build with us, with another supplier or in-house — the documents and prototype are yours either way. We can also review other suppliers’ proposals against the plan.
Help with whichever route you choose
Engagement models
How AI consulting is priced.
Most consulting runs as a fixed-fee discovery sprint. A broader review across several teams or sites is quoted as a fixed-price project, and if you choose to build with us afterwards, a managed team can take the roadmap forward.
Discovery sprint
From £2,000 fixed fee
You have an idea or a problem, but not yet a scope you would trust a quote against.
- Workshops with your team
- Prototype or technical spike
- Written scope, plan and estimate
Fixed-scope project
Quoted after discovery
A defined build — an MVP, a rebuild or a feature set — with milestones and a fixed budget.
- Agreed milestones and deliverables
- Milestone-based billing
- A demo at the end of every sprint
Managed product team
From £9,500 per month
A small cross-functional pod — engineering, QA and delivery lead — that owns an outcome.
- One accountable delivery owner
- Sprint planning and reporting
- QA and code review included
Prices are in GBP. Every estimate is confirmed in writing after a discovery call — the figures above are where engagements start, not a quote.
Why Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
AI consulting, straight answers.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
What does an AI consulting engagement include?
Workshops and interviews to gather use cases, a readiness review of your data, processes, people and systems, a ranked shortlist with build-or-buy calls, a business case in your own measures, a governance pack and a working prototype of the strongest idea. You keep every document and the prototype.
How much does AI consulting cost in the UK?
A focused engagement runs as a discovery sprint, from £2,000 as a fixed fee. Broader reviews across several teams or sites are quoted as a fixed-price project once we understand the scope. Either way you know the cost before we start, rather than facing an open-ended day rate.
You also build AI software. Is your advice independent?
Not in the sense of a firm that never builds, and we would rather say so. What we offer instead is a fixed fee, written reasons for every recommendation, no ties to any AI vendor, and outputs you can take to any supplier. “Buy this product” and “not yet” are answers we give whenever they are the right ones.
Do we need clean data before we start with AI?
No. Perfect data is rare, and waiting for it stalls everything. The readiness review shows which use cases your data can support now, which need specific clean-up first, and what that clean-up involves — so you start where you are rather than with a data programme.
Do we need an AI policy for our staff?
If your staff can reach public AI tools, a short policy is sensible. It should say which tools are approved, what data must never be entered, who to ask about new uses and how outputs are checked. We draft a practical version for your HR and legal teams to review and adopt.
How do you work out the ROI of an AI use case?
By measuring before promising. For each shortlisted process we record volume, time per item, error and rework rates and delays, then estimate the full cost of the AI version: build, model fees, hosting, review time and support. The prototype then tests the key assumptions on your own data, so the business case does not rest on vendor figures.
Why build a prototype during a consulting engagement?
Because the riskiest assumptions in an AI plan — accuracy on your data, cost per task, whether staff trust the output — cannot be settled in a workshop. A small prototype tests them cheaply, and your board sees something working rather than a slide.
How do you handle AI risk and governance?
Practically: a staff use policy, human oversight designed into each use case, vendor questions on data retention and processing location, DPIA inputs following the ICO’s guidance, and a short risk register with named owners. Sign-off stays with your DPO, legal and compliance advisers — we do not give legal advice.
Can you work directly with our board or leadership team?
Yes. Workshops and presentations run by video call. The aim is a leadership team that understands the trade-offs well enough to make the decision itself, not one that has been sold a plan.
What happens after the consulting engagement?
You choose. We can deliver the roadmap as a fixed-price pilot or with a managed team, you can take it to another supplier, or your own team can deliver it — everything is documented so any of those works. We are also happy to review other suppliers’ proposals against the plan.
Start a project
Need an AI plan you can test?
Tell us where AI keeps coming up — a board question, a team asking for tools, a process that eats hours. We will suggest how to scope a first engagement and what it would cost.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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