Evidence-backed pricing decision workspace

Reduce manual price-checking while keeping every price change defensible. For pricing managers and e-commerce operators setting and adjusting product prices, convert permitted competitor listings, sales history, cost data and margin rules into reviewed price recommendations linked to evidence. The benefit is a testable hypothesis, measured through accepted price changes per pricing hour and margin after change; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted competitor listings, sales history, cost data and margin rules, then follow this sequence: 1. Import product catalog and cost data. 2. Monitor permitted competitor listings. 3. Suggest prices from demand, cost and market signals. Resolve uncertain cases with qualified reviewers, approve reviewed price recommendations linked to evidence, and measure accepted price changes per pricing hour and margin after change against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved sales channel and permitted competitor sources; final price approval and margin checks remain commercial. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve pricing authority, source attribution, data accuracy and usage permissions. Buyers approve substantive price changes and rollout scope. One approved sales channel and permitted competitor sources; final price approval and margin checks remain commercial. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One approved sales channel and permitted competitor sources; final price approval and margin checks remain commercial. Implement one approved input format, a bounded representative case set and the first two task modules: import product catalog and cost data; monitor permitted competitor listings. Support the third module with operator review: suggest prices from demand, cost and market signals. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Buyer-owned product catalogs, authorized sales history and permitted competitor sources. Cloud data storage, spreadsheet import/export and sales platform destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Product and rule setup, Editable pricing workspace, Review and change log. Use a thumbnail gallery for product groups, a large central table of products with current and recommended prices, and a right-hand panel for competitor evidence, margin rules and comments. Let users compare scenarios side by side. Display draft, changes requested and approved states. Provide a review link with comments anchored to the relevant product or competitor listing. Make the task-specific outcome reviewed price recommendations linked to evidence visible beside its evidence, review state and value baseline.