AI Proof of Concept and Prototyping
Test an AI idea on your real data before you commit to a full build. In 3 to 4 weeks you get a working prototype, an accuracy and cost report, and a clear recommendation on whether to go ahead.
Problems we solve
You have an AI idea but no evidence it will work on your data.
A full build is a big commitment without proof.
Internal experiments stalled without a clear result.
What we build
- Working prototype Something your team can use and test, not slides.
- Evaluation report Accuracy measured against real cases, with examples of failures.
- Cost and speed estimate What it will cost to run at your volumes.
- Go or no-go recommendation Our honest view, with a production estimate if it is a go.
How it works
- Define Agree the question the prototype must answer and how to measure it.
- Build Prototype on a sample of your real data.
- Test Measure accuracy, speed and cost against real cases.
- Decide Review the results together and choose the next step.
How to start
A prototype sprint runs 3 to 4 weeks. Tell us the idea and we will tell you whether a prototype is the right first step.
Questions about AI Prototyping
What is the difference between a prototype and an MVP?
A prototype proves an AI approach works on your data and may be thrown away. An MVP is the first version of a product that real customers use.
What if the prototype shows it will not work?
That is a useful result. You will know why, what it would take to make it work, and you will have avoided a costly full build.
Can the prototype become the production system?
Parts of it often can. We tell you what is reusable and what needs to be rebuilt for production.
How much of our data do you need?
Usually a representative sample, not everything. We agree how it is shared and protected before we start.