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AI consultancy that builds what it recommends
An AI consultancy helps a business decide where AI will pay and then puts it to work. predeploy does both: a readiness assessment and use-case selection, then the build and deployment in your stack. The engineers who scope the work are the ones who write the code.
Our AI consulting services cover strategy, integration and adoption across teams. Scope one workflow first, with an owner and evidence for the release decision.
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
What an AI consultancy does
AI consultancy: what turns adoption into use?
Most businesses have tried AI. Far fewer run it in the work that matters. An AI consultancy closes that gap by choosing the right workflows and getting them into daily use.
The ONS, Business Insights and Conditions Survey, wave 159 found that 29% of UK businesses used at least one AI technology in June 2026. Among businesses with ten or more staff that use AI, only one in ten say they use it extensively.
That second number is the one we work on. We take one workflow at a time from trial to daily use, and we start with the ones closest to revenue.
Source: ONS, Business Insights and Conditions Survey, wave 159 (Jul 2026)
Source: ONS, Artificial intelligence in UK businesses: 2023 to 2026 (Jul 2026)
Readiness assessment
AI consultancy: are you ready to build?
Tick what is true today. The needle shows where most businesses in your position should start.
A full readiness assessment goes further. We look at the processes, the data behind them, the systems they touch and the rules that apply, from data protection to sector regulation.
You get a plain answer: which use cases can start now, which need data work first, and which to drop.
Tick what applies
The needle moves with each answer.
Use-case selection
AI consultancy: which use case comes first?
Every candidate use case gets the same five questions. The ones that score well on all five go first.
Good first use cases are narrow, frequent and checkable: a task people do many times a week, with an output someone can verify quickly.
When scores are close, we put revenue-facing work first. It shows up in numbers the board already reads.
| Criterion | What we ask |
|---|---|
| Value | Does it save time, win revenue or cut cost in a way someone already measures? |
| Feasibility | Can current models do this task reliably, with checks we can test? |
| Data | Is the input data available, legal to use and good enough today? |
| Risk | What happens when it is wrong, and who catches it before a customer does? |
| Ownership | Who on your side will run it, and do they want it? |
AI implementation
AI consultancy: how does the build run?
AI implementation means connecting models to your data and tools and putting the result in front of the people who use it. We build inside the systems you already run, not in a separate pilot environment.
Each build ships with evaluation tests, so you can see how often the workflow gets it right before it reaches a customer. It goes to a small group of real users first, then wider.
Your people sit with our engineers throughout. By the end, someone on your side knows how it works and how to change it.
An illustrative plan, not a promise or a measured average. We scope the real timeline on the first call.
AI consulting, two ways
AI consultancy: advice or implementation?
Flip the switch to compare a strategy-only engagement with a forward-deployed one.
The bars compare approaches, not measured results. A strategy-only engagement can be the right call when you already have an engineering team with time to build. If you do not, the plan tends to wait.
A forward-deployed AI consultant carries the work from the first interview to the running workflow. What a forward deployed engineer does →
How it works
Four stages, one team
Each stage ends with something you can use. You can stop after any of them.
Readiness assessment
Look at the work, the data and the people
We interview the teams, review the systems and data, and check the security and data-protection rules any build must respect.
Use-case selection
Score every candidate, keep the few that pay
Each use case is scored on value, feasibility, data and risk. Revenue-facing work goes to the top when the scores are close.
Build
Build the first workflow in your stack
Our engineers connect models to your data and tools, write evaluation tests, and ship to a small group of real users first.
Deploy and hand over
Roll it out and leave it with an owner
We widen the rollout, train the people who use and maintain it, and hand over the code and documentation.
Worked example
An AI product we built
We run our own AI in production. RFP Quest is the clearest example of the kind of system we build for clients.
RFP Quest is our RFP software for UK public-sector bids. It turns a large, messy stream of public notices into a pipeline a sales team can work, and it drafts the first version of each response.
The same pattern fits many businesses: take a document-heavy process, structure the data, add a model where judgement is cheap to check, and keep a person in charge of the final call.
- Reads every notice published on Find a Tender and Contracts Finder
- Matches tenders to what a supplier actually sells
- Forecasts when public buyers are likely to re-tender a contract
- Drafts bid responses with AI, for a person to edit and submit
What we do and do not do
Clear lines, from the start
An honest AI consultant tells you what AI will not fix. These are the lines we hold.
If the assessment shows a use case is not ready, we say so and suggest what would make it ready. Sometimes that is data work. Sometimes it is training a few people first.
- We do assess, select, build, deploy and train
- We do put a person in the loop wherever an error reaches a customer
- We do hand over code, documentation and ownership
- We do not resell software or take commission on tools
- We do not build what your data cannot support
- We do not stay on once your team can run it
A practical next step
AI consultancy: which decision should come first?
Separate readiness, feasibility and evaluation before committing to a build.
An AI readiness consultant checks whether the people, data and systems can support the intended use. AI feasibility study consulting tests whether one proposed workflow is practical. An AI evaluation consultant establishes how you will judge its output. These are connected decisions with different deliverables.
The AI implementation consulting overview connects those stages. For an illustrative tender response assistant, readiness checks access to approved evidence, feasibility tests a representative question and evaluation checks whether the draft is supported by that evidence. A missing source should remain visible rather than becoming an invented answer. The business owner decides the acceptable review burden before wider use.
A bounded AI consultancy project can then have a clear sequence, exclusions and release conditions. Planning support establishes that order, technical consultancy reviews the architecture and on-demand consulting can address a specific blocker. Decide who will operate the workflow and reserve time for implementation support and training before the first release.
Prepare the buying decision with business use cases, an AI business case and the consulting cost guide. Use the readiness checklist before a pilot, then follow an implementation roadmap. Governance consulting establishes who approves changes and what evidence they need.
For revenue workflows, use AI GTM planning to define the task, then compare an AI powered GTM platform with a custom integration before commissioning a build.
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AI implementation consulting
Assess readiness, select a project, evaluate it and deploy.
Start with the decision
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