What does your product demo prove?
Show one real flow and explain what the product and user each do.
The question
You are preparing a demo of an AI purchase-order intake product. The demo uses synthetic purchase orders. A user checks the fields before the export.
| Input | Export after approval | |
|---|---|---|
| Purchase order | PO-103 | PO-103 |
| Quantity | 24 | 24 |
| Approval | Required | Recorded |
The team tested this sample order and generated the export shown here.
“What does your product demo prove?”
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A strong answer, step by step
Open each step in turn. This is one strong answer, not the only one.
Step 1: Which path would you show?
What would you do here? Open the move you’d make.
Show the finished export and say the model did everything.
Tempting, but it misses something. The export is real in this example. It still passed a user approval step.
Show the file, extraction, approval and export in order.
The strong move. This gives the investor one complete example of the working flow.
Lead with a mockup of what the product will support next.
Tempting, but it misses something. The question asks what works today. A mockup illustrates an idea.
See how a strong candidate says it
“Open PO-103, run the extraction, show the approval and open the exported record.”
Why it works: The investor can see the input, the point where the user acts and the result.
Step 2: What would you say about the test?
What would you do here? Open the move you’d make.
Name the synthetic input and the approval step.
The strong move. The audience can tell which evidence the example supplies.
Say the product is accurate on purchase orders generally.
Tempting, but it misses something. One synthetic order does not establish performance on representative inputs.
Skip the approval step because extraction is the interesting part.
Tempting, but it misses something. The approval determines whether the data can be exported.
See how a strong candidate says it
“We extracted fields from this synthetic purchase order. A user checked the result before export.”
Why it works: The sample shows how the flow works under stated conditions. One sample gives no accuracy estimate.
Step 3: What would you show when extraction fails?
The investor asks to see a low-quality scan. The extracted quantity is blank. The audit log records that a founder entered the quantity manually before approval.
What would you do here? Open the move you’d make.
Show only the final export and say the scan passed.
Tempting, but it misses something. The correct output depended on manual entry.
Show the missing field, manual entry and approval.
The strong move. This makes the working fallback and the model failure visible.
Drop the second scan and say the first scan proves broad coverage.
Tempting, but it misses something. The first example does not cover the failure in the second scan.
See how a strong candidate says it
“Show the blank quantity in the extraction. Then show that a founder entered the value manually before the user approved the export.”
Why it works: The audit entry identifies the source of the value. The export still demonstrates the fallback.
Step 4: Which test would you run next?
What would you do here? Open the move you’d make.
Measure final export quality and ignore who corrected the data.
Tempting, but it misses something. That can assess the final output. It cannot tell you which fields the model extracted.
Record another clean demo and use it as the quality test.
Tempting, but it misses something. A second clean example leaves the poor-scan condition untested.
Verify field accuracy and record corrections on representative scans.
The strong move. The result can inform both model changes and the review workflow.
See how a strong candidate says it
“Test a representative set of low-quality scans. Check each field against the source and record each manual correction separately.”
Why it works: The records will show which fields the product handled and how much correction the workflow required.
The answer, in short
- Decision
- Show the complete workflow, including the manual fallback.
- Because
- The audit log traces the corrected value to a person.
- What would change it
- Representative test results that show which fields need manual entry.
Common traps
- Describing the finished export as model output.
- Generalizing from one clean example.
Check your answer
- What input did you use?
- What did the product extract?
- What did a person enter or approve?
- What would you test next?
Last reviewed Nov 11, 2026