An unproven idea
You can see the potential but don't yet know whether AI will work with your data and processes.
AI Lab — Prove the value
Experiment with ideas, build working prototypes and test the business case before making a larger commitment.
Start a conversation →Who it's for
A practical response to your business challenges
For businesses with a promising use case that needs practical evidence of technical feasibility, user acceptance and financial value.
Investment risk
A full implementation feels premature without a realistic demonstration.
Technical uncertainty
You need to understand reliability, accuracy, integration and human oversight.
What we do
How we work with you
1. Define the hypothesis
Agree the business problem, baseline measures and test success criteria.
2. Design the prototype
Select appropriate AI models, tools, workflow steps and safe test data.
3. Build and demonstrate
Create a controlled proof of concept with representative tasks and users.
4. Evaluate and recommend
Assess results, costs, risks and requirements for production deployment.
Business benefits
What your organisation stands to gain
Reduce uncertainty
Find limitations early before spending on a full build.
Make AI tangible
Let stakeholders see and test a working solution.
Improve the business case
Use test evidence to refine cost, benefit and delivery estimates.
Deliverables
What you'll receive
Working prototype
A demonstration of the target process or selected capability.
Evaluation report
Test results against agreed quality, reliability and efficiency criteria.
Implementation outline
Architecture, dependencies, controls and indicative build scope.
Go / no-go recommendation
A clear decision point supported by evidence.
Engagement model
AI Proof-of-Value Sprint
Typically two to four weeks. Each engagement starts with agreed scope, responsibilities and success criteria.
Indicative £5,000–£15,000. Pricing is an initial hypothesis, not a fixed public quote.
Ready to explore AI Proof-of-Value Sprint?
We'll discuss your business objectives and recommend a proportionate starting point.
Discuss your requirements →Why use the Lab?
Prove an idea before committing to a full build
A polished demonstration can be misleading. The real test is whether a solution handles representative work accurately, safely and at an acceptable cost.
Test the difficult cases
Use realistic examples, exceptions and failure scenarios rather than only the easiest inputs.
Understand the economics
Measure handling time, model and integration costs, quality and the level of human intervention required.
Build stakeholder confidence
Let employees and decision-makers experience the proposed workflow and challenge assumptions.
Proof-of-value sprint
What happens during a typical sprint
01
Define
Agree a narrow use case, baseline, representative data and measurable success criteria.
02
Prototype
Build the smallest working solution that can test the business hypothesis.
03
Evaluate
Measure output quality, speed, reliability, cost and human review requirements.
04
Decide
Present evidence, limitations and a recommendation to proceed, revise or stop.
Examples to test
Ideas suited to a focused Lab sprint
Customer enquiry triage
Can incoming emails be categorised and routed accurately enough to reduce manual sorting?
Document extraction
Can an AI-assisted workflow extract fields from varied documents and flag uncertain results?
Internal knowledge search
Can employees find reliable answers with citations while respecting document permissions?
What you'll receive: A working prototype, test findings, risks and dependencies, a production implementation outline and a clear go/no-go recommendation.
Have an AI idea worth testing?
We'll help define a focused experiment with evidence you can use to make the next decision.