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Whitesoft

Advisory · AI Prototyping

Prove it works on your data before you build it properly.

A prototype exists to answer specific questions: does the model perform acceptably on our real documents, what does it cost per transaction at volume, will the team actually use it? We scope prototypes around those questions and stop when they are answered.

Scope a prototypeSee where this fits in our method

What you get

  • 01

    Measured performance on your data against agreed acceptance criteria

  • 02

    A realistic unit-cost estimate at production volume

  • 03

    A go, adjust or stop recommendation with the evidence behind it

How we do it

The same discipline every time: understand the business, quantify, test, then commit.

Deliverables

  • Working prototype on your data
  • Evaluation report with accuracy, cost and latency
  • User feedback summary
  • Production design recommendations

  1. Define the hypothesis and pass mark

    Before any code, we agree what the prototype must demonstrate and the thresholds that would justify production investment.

  2. Build the smallest useful thing

    Using real data and a representative slice of the workflow, we build enough to test the hypothesis. Throwaway code is acceptable here; unclear results are not.

  3. Evaluate rigorously

    We run structured evaluation sets, measure cost and latency, and put the prototype in front of the people who would use it.

  4. Recommend the path

    You receive the evidence, the production design implications, and a clear recommendation.

Questions we are often asked

Will the prototype become the production system?

Sometimes parts of it will. But a prototype is optimised for learning and production is optimised for reliability, security and cost. We are explicit about which is which.

Related

Use cases this service supports

Scope a prototype.

A short conversation is enough to tell whether this is the right starting point for you.

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