Pricing and quotation guide
AI proof of concept development cost
A proof of concept should be cheap relative to the decision it informs. Its cost comes mainly from preparing a good test set and running a minimal prototype against it; there is no need for integrations, polished screens or production-grade error handling.
This page explains what a proof of concept costs and how to keep it small enough to be worth doing.
Illustrative diagram
Proof of concept
- Framing
- Question
- Criteria
- Test set
- Examples
- Labels
- Prototype and report
- Minimal build
- Results
- Excluded
- Integrations
- Interfaces
What drives proof of concept cost
- Framing
- Agreeing the question, criteria and data needs.
- Test set size and variety
- More examples and formats take longer to prepare and run.
- Labelling
- Recording correct answers for each example; your team can share this.
- Prototype
- Minimal set-up to run the AI step on the test set.
- Iterations
- A small number of prompt adjustments within the agreed scope.
- Reporting
- Results, failure analysis and recommendation.
Costs paid to third parties (separate from our fees)
- AI usage during testing, usually small
- Your team's time preparing examples
AI usage during a proof of concept is usually modest and is charged to your account or listed in the quotation.
What a proof of concept leaves out
| Excluded | Why |
|---|---|
| Integrations with live systems | Not needed to answer the question |
| Review screens and user interfaces | Results can be checked in a spreadsheet |
| Production error handling | Built later if the answer is go |
| Ongoing monitoring | Belongs to a pilot or live workflow |
Keep it proportionate
If a proof of concept starts to grow, adding integrations, interfaces and extra questions, it becomes an expensive first version of the product instead of a cheap test. Holding the line on scope keeps the cost low and the decision clear. Additional questions can each have their own small proof of concept if needed.
A useful rule is that the proof of concept should cost a small fraction of the full build it might lead to.
For an accurate quotation
Describe
- The single question
- The data type and variety
- What success would look like
Arrange
- Who will help prepare examples
- Any data restrictions
- When you need the answer
Example: testing email triage
Illustrative example — not a client case study
Situation: A business wants to know whether AI can sort support emails into its existing categories.
- A representative sample of past emails is anonymised and labelled by the support team.
- A minimal prototype classifies the sample; results are compared with the labels.
- The report shows strong results for most categories and confusion between two similar ones.
What this shows: The recommendation was to adjust by merging the two categories before a pilot. This example is illustrative.
What happens next
If the answer is go, the next step is a pilot with real integrations and review steps, quoted separately using the proof of concept's measurements. If the answer is adjust, a small follow-up test may be enough. If the answer is stop, there is nothing more to pay, and the test set remains available for a future re-test.
Why it is worth paying for
The value of a proof of concept lies in the decision it makes possible. A full AI build can cost many times more than the test, and building something that turns out not to work wastes that money as well as staff time. Spending a small amount first to find out whether the idea works, and what it would cost to run, is usually good value even when the answer is no.
The measurements it produces also make any following quotation more accurate.
Timing and effort
Most of the elapsed time in a proof of concept goes into preparing and labelling the test set. Having examples ready before the engagement starts shortens it considerably and reduces cost.
Setting a budget
A sensible budget for a proof of concept is one you would be comfortable spending to learn that an idea does not work. If that number feels too small for a meaningful test, narrow the question further rather than enlarging the budget.
Questions about AI proof of concept development cost
Why is a proof of concept cheaper than a build?
It only answers one question and leaves out integrations, interfaces and production features.
Can our team reduce the cost?
Yes, by helping prepare and label examples.
Is AI usage expensive during testing?
Usually not; test volumes are small.
What if we want to test several ideas?
Give each its own small proof of concept.
Is there a cost if we stop?
No further cost; you keep the test set and report.
Does a successful result mean the build is cheap?
Not necessarily. The build adds integrations and reliability work, quoted separately.
Is a proof of concept worth it if the answer might be no?
Yes. A no answer saves the cost of building something that would not work.
Is the report included?
Yes, with results, failure examples, cost estimates and a recommendation.
Can a proof of concept be extended if results are close?
A small follow-up test can be agreed for specific adjustments.
How do we set the budget?
At an amount you would accept spending to learn the idea does not work.
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