Services
AI & Data Consulting
We establish whether a problem is genuinely solvable with AI before recommending it, then build the data foundation the solution depends on.
In this engagement
- An assessment covering data availability, process maturity, build-versus-buy options and a realistic benefit case
- A signed-off solution blueprint with architecture, data flows and integration boundaries
- A working model or pipeline with documented training data, evaluation results and known limitations
- A handover pack your team can operate and extend without us
Most AI proposals we are asked to review begin with a model and end with a data problem. Ours begin the other way round: what decision are you trying to improve, what evidence would change it, and is that evidence actually available to you in a usable state?
Where the answer is that AI is the wrong tool — often because the underlying process or data is the real constraint — we will say so in the assessment. That conversation costs you one engagement and saves you one failed project.
How engagements start
- A fixed-scope readiness assessment, typically two to four weeks
- A pilot on one workflow before any production commitment
- Production build scoped once the pilot's evaluation criteria are met
Capabilities
What ai & data consulting covers
The specific work we carry out. Anything not listed here is still something we can scope — ask.
AI readiness and data maturity assessment
Predictive and statistical model development
Conversational assistants and document intelligence
Model deployment, monitoring and governance
Questions
What clients ask us first
If your question is not here, ask it directly — we would rather answer it before you commit than after.
What data do you need from us to start?
Enough to characterise the problem, not a data lake. A representative extract with field definitions and a description of how it is produced today is enough for a readiness assessment. We will tell you exactly what we need before we ask for it.
How do you decide whether AI is appropriate?
Three tests. Is the outcome predictable enough for a model to beat the current process measurably? Is there enough labelled or self-supervising data to train and evaluate one? Is the cost of being wrong higher than the cost of not doing it? If any answer is no, we recommend a rules-based or process fix instead.
Do you hand over models at the end?
Always. Every model ships with its training data provenance, evaluation results, known failure modes and retraining procedure. If you would rather we operate it under a support agreement, that is available too, but it is never the only option.
Where we apply it
Sectors this service is used in
The same engineering, applied against the constraints of your sector.
- Banking & Financial ServicesLending and servicing systems that survive audit, and data foundations that make automated decisions defensible.
- ManufacturingPlant-floor systems that work when the network does not, and models that read the machines properly.
- Retail, CRM & DistributionOne customer record across every channel, with campaigns and forecasting running off the same data.
- Logistics & Supply ChainVisibility from dock to doorstep, and the planning models that decide what to move and when.
- Healthcare & Life SciencesPatient-facing and clinical systems built around consent, privacy and the realities of clinical workflow.
- InsurancePolicy administration and claims automation, with underwriting models a regulator can interrogate.
Ready to talk about ai & data consulting?
Send us the problem in whatever detail you have. A senior engineer replies within one business day, and you will get an honest read on whether we are the right partner for it.