Prepare pricing models for AI analysis
Pricing workbooks can expose a business without containing any personal data. Discount floors, margin assumptions and customer exceptions may be the sensitive material. A formula or scenario question should not automatically carry the commercial strategy behind the model.
For Commercial finance and pricing operations teams
Synthetic example: a discount threshold
A fictional pricing analyst wants to explain a discontinuity at a volume threshold. The example uses invented quantities, costs and discounts. It is not a live offer, a customer result or a recommendation to change commercial rates.
What you are working with
- An XLSX model with tier tables, cost assumptions and margin formulas.
- Customer-specific overrides stored on a hidden sheet.
- Comments explaining discount discretion and negotiation exceptions.
A safer approach
- Build a fresh fixture that reproduces the threshold behavior with invented values.
- Retain formula relationships and units while omitting real customers and approval limits.
- Specify expected results on each side of the threshold before asking for analysis.
Expected outcome: The assistant explains the synthetic calculation and proposes a testable adjustment. Commercial owners independently validate any change and decide whether it belongs in the operational model.
Work through the procedure
Identify what is confidential
Review both the values and the model's logic. A formula can reveal discount strategy even when every customer name is removed. Define whether the AI task needs mathematical debugging, scenario presentation or actual commercial judgment; approve each purpose separately.
Build a bounded fixture
Reproduce the relevant tier boundary, lookup behavior and unit conventions with invented inputs. Include cases just below, at and above the threshold. Avoid copying whole sheets with hidden overrides when a small new table and a few formulas can reproduce the problem.
Inspect dependencies and notes
Check named ranges, external links, hidden sheets, comments and chart data. Verify that the fixture cannot retrieve operational prices. If replacing values changes the behavior being investigated, document the limitation and consider whether a more abstract explanation can answer the question.
Validate proposed changes
Recalculate the synthetic test cases independently and confirm currencies, units and rounding rules. Check that the adjustment does not create new discontinuities. Keep the generated formula separate from the operational model until the responsible commercial reviewer approves its use.
What to check before proceeding
1. Commercial scope
- Ready when
- The shared material excludes unapproved real rates, exceptions and strategic logic.
- If the check fails
- Abstract the question further or keep the task inside authorized tools.
2. Fixture independence
- Ready when
- No lookup or external connection references operational pricing data.
- If the check fails
- Rebuild those dependencies with synthetic inputs before submission.
3. Boundary behavior
- Ready when
- The proposal passes cases around every relevant threshold with correct units.
- If the check fails
- Reject or revise it before any change reaches the pricing process.
Common mistakes to avoid
- A PII-free workbook can still disclose commercially critical strategy; absence of identifier detections is not approval to share.
- Changing the numbers without checking branch conditions can erase the original pricing problem and make the AI answer irrelevant.
Evaluate this workflow with Aona
Where Aona can help
Evaluate Aona's configured prompt and file policy with synthetic pricing examples on the actual submission route. Supported XLSX, DOCX and PDF formats still have provider-specific and browser/native differences.
What to confirm
Do not assume Aona automatically recognizes a confidential margin floor or validates model economics. Policy processing uses a regional backend; document approval and calculation review remain with the business.
Managing this document workflow across a team?
Review your AI tool, document format and data-handling requirements. Use a synthetic example to discuss supported controls and the checks your team still needs to perform.