Overview
Many AI projects collapse once someone finally tests whether the data backs the idea, so we do that test up front. You bring the use case; we study the records you really hold and train a simple baseline to see how much predictive signal exists. Sometimes the answer is a model worth tuning, and sometimes plain business rules deliver nearly as much for much less money. In both cases, your decision rests on evidence.
What you receive
- A review of data volume, quality, labels and balance between classes
- A baseline model trained and scored using your real data
- A head-to-head test of the model versus straightforward business rules
- Analysis of which features carry most of the predictive value
- Estimated budget and effort bands for building a production version
- A written recommendation, which could honestly be to stop here
- Reproducible notebooks and code that you own outright
How it runs
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Step - 01
Define
We agree what is being predicted, which decision it informs and what success means.
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Step - 02
Audit
Your data is checked for gaps, leakage and the quality of its labels.
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Step - 03
Baseline
A first model is trained and compared against the simple rules option.
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Step - 04
Report
We walk you through results, figures and a build estimate on a call.
Timeline: 10 working days, counted from the day we receive what we need from you.
Is it a fit?
A good match for
- Teams under pressure to use AI with no clear use case yet
- Organisations with a specific prediction need and unproven data
- Boards that want a clear case before hiring a data scientist
What we need from you
- An extract of your data, with confidential columns masked if needed
- A plain explanation of which business decision should get better
- Time with the colleague who understands how records are captured
Why it helps
- A clear proceed-or-stop call, grounded in baseline results
- Data quality issues are found before they can sink a project
- Occasionally a simpler solution that avoids a model entirely
Questions
You get that conclusion along with the evidence behind it, which is far cheaper than finding out mid-build. We would rather tell you not to proceed than build something that will fail.
It varies by problem, but for most classification tasks a few thousand labelled records is a sensible starting point. We confirm what you have during the audit.
Yes, and we sign it before any data changes hands. If you ask, we also delete every data extract once the work has finished.