Prove it works on your data in 2‑4 weeks
A demo on sample data proves nothing. We build a working prototype on your real data, measure it against agreed metrics, and finish with a clear go or no‑go before you commit to a full build.
Pilots that impress, then stall
of AI proofs of concept were scrapped before reaching production, on average, in a survey of companies in North America and Europe.
Source: S&P Global Market Intelligence, Voice of the Enterprise, 2025
What goes wrong
- Prototypes built on clean sample data that does not reflect reality
- Success judged by a demo, not by measured accuracy
- Running costs discovered after launch
- No decision point, so pilots drift for months
What we do instead
- Prototype on your real, messy data from day one
- Agree metrics and an evaluation set before building
- Model cost per task and latency alongside accuracy
- End every sprint with an explicit go or no‑go
Who it's for
Product managers
You need evidence that an AI feature works before it goes on the roadmap.
Enterprise innovation teams
You need a pilot that produces a decision, not another slide deck.
Founders
You need proof for investors that your AI approach holds up on real inputs.
How it works
Typical timeframe: 2‑4 weeks. Every engagement is scoped to your situation after the discovery call.
Define success
We agree the task, the users, the metrics that matter and the threshold for "good enough", then build an evaluation set from your real examples.
- Success metrics and thresholds
- Evaluation dataset
- Baseline of the current process
Build the prototype
We build the smallest working version on your data, compare approaches and models, and put it in front of a handful of real users.
- Working prototype
- Model and approach comparison
- User feedback from real sessions
Measure and decide
We report accuracy, failure modes, cost per task and latency against the thresholds we agreed, and recommend go, adjust, or stop.
- Evaluation report
- Cost and performance model
- Go or no‑go recommendation with build plan
You leave with
- A prototype you can show to users, investors or your board
- Measured accuracy on your own data, not a vendor benchmark
- A cost-per-task estimate at your expected volume
- A clear decision and a plan for what comes next
What we test
- Whether an agent is needed, or a simpler workflow does the job
- Which model gives the best accuracy for the cost
- Where humans need to stay in the loop
- How users react to imperfect AI output
Our founder validated an AI credit-health model for SMEs in 2 weeks at Creditit.AI, testing demand with live businesses first. That validation landed the first lender partnership.
See case studiesCommon questions
What do you need from us to start?
Access to a representative sample of real data, one person who knows the workflow well, and a few users willing to try the prototype. Where personal data is involved, we put a data processing agreement in place before we start.
What if the prototype does not meet the threshold?
Then the sprint did its job. You learn this for a fraction of the time and effort of a full build. We explain what failed, and whether a different approach, more data, or no AI at all is the better path.
Is a validation sprint the same as a pilot?
It is a pilot with a fixed end date and a decision built in. Many pilots drift because nobody defined what success looks like. We define it on day one.
Can our own team build after the sprint?
Yes. The code, prompts and evaluation set are yours, with no license fees or lock-in. We document everything so your team or another partner can take it forward.
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AI agents for well-defined workflows, running in your systems, with human escalation, audit trails and measured savings.
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Proportionate AI governance for the EU AI Act and GDPR, plus hands-on AI training for your team on the tools they actually use.
Get a decision, not another demo
Tell us about the use case you are considering. In 30 minutes we will tell you whether it is ready to validate and what we would test first.