AI quotation organization and discrepancy review: an FDE plan for home-service teams
Similar item names, different units and missing notes can make quote comparisons misleading. An FDE connects enterprise AI to extraction, source locations, scope checks and human confirmation for traceable quotation workflows.
Two quotes list cabinets, but one prices by linear metre and another by set. Installation is included in one and appears separately in the other’s notes. A column of amounts hides these scope differences. Establish comparability before summarizing discrepancies.
AI assists reading and classification. Calculations follow business-approved rules. An FDE implements source links, exception flags and outputs; staff confirm specifications, scope and final quotations.
Prepare documents and comparison rules
- Quote files and versions for the same project, identifying whether the purpose is supplier comparison, scope revision or historical verification.
- Define area, item, specification, quantity, unit, unit price, subtotal and original notes.
- Record currency, stated tax treatment, installation, transport and other inclusions or exclusions. Unstated scope stays pending confirmation.
- Agree fields eligible for automatic matching and fields requiring sales, design or project-owner review.
Simulated comparison: scope before amounts
| Source condition | What to retain | Review conclusion |
|---|---|---|
| Quote A prices cabinets per linear metre; B prices per set | Both original units and source rows without an invented conversion | Measurement scope is unconfirmed; defer unit-price comparison. |
| A includes installation; B notes a separate installation charge | The note linked to its applicable item | Confirm installation scope before comparing total coverage. |
| A revised quote adds an accessory absent from the old version | Added item, specification, quantity and version | Flag an addition for the owner to confirm against the revised design. |
| A subtotal differs from the result of the agreed calculation | Original amount, calculated amount and discrepancy | Check rounding, discounts or extraction errors without overwriting the original. |
These are simulated document fragments with no real supplier, actual price or purchasing recommendation.
How an FDE builds a maintainable workflow
Extract and preserve original values
Recognize fields and table relationships with source locations. Store normalized values separately so original wording remains available.
Assess comparability
Propose matches based on area, item, specification, units and scope. Leave ambiguous matches for staff to select.
Check calculations and notes
Apply business-approved rules to subtotals and show missing, duplicate, added, removed and scope-different items. Model confidence is only a review signal.
Export or write after confirmation
Deliver a source index, organized table and pending-review list. Keep destination IDs, receipts and versions when writing to systems to prevent duplicates.
Test six exception types
| Input issue | Expected handling |
|---|---|
| Tables crossing pages or merged cells | Verify item and note ownership; flag uncertain relationships. |
| Blurred scans or questionable amounts | Expose the source location and uncertainty for key-field review. |
| Mixed units or missing specifications | Preserve original values without unsupported conversions or equivalence assumptions. |
| Discounts or additional charges in notes | Display at the applicable level and record whether included in calculations, avoiding double counting. |
| Repeated file upload | Identify the file and version and avoid duplicate organized records. |
| Source changes after confirmation | Retain the confirmed version and identify fields and items requiring renewed review. |
Deliverables and responsibilities
The FDE delivers a field and unit dictionary, source-location index, organized quote and discrepancy tables, a pending-review workflow and export or system-write instructions. Business owners check fields, comparable scope, calculations and notes before approving commercial content.
Microsoft’s Document Intelligence documentation describes confidence as a signal for deciding when human review is needed and notes that some fields do not return confidence. Verify actual outputs if using that product; this proposal still tests key fields against business requirements.
Pilot one quotation template
Use de-identified samples containing ordinary tables, notes and version changes. Record current preparation and review time. Test mismatches, omissions and rework alongside speed; extracted row counts alone are insufficient.
Bring one recently discovered quote discrepancy. An FDE uses it to agree initial fields, integrations and exception tests. Make this quotation workflow dependable before expanding enterprise AI to other suppliers and formats.
Related scenarios and services
Start with one actual business task
Bring two de-identified quotations, their project scope, unit conventions and one actual discrepancy. An FDE maps original lines, extracted fields and review comments before agreeing integrations and pilot scope.
Discuss AI-assisted quotation organizationThis is an FDE implementation proposal. Examples are simulated and do not represent completed client projects or measured results. Agree features, integrations and deliverables before the pilot.