Case studyLive in production

Competitor price intelligence and dynamic pricing

Agents that find the real price behind every offer and tell the pricing team what to do next.

Visual for competitor price intelligence and dynamic pricingA leading home appliances company
Problem statement

What the client was up against

Competitors were undercutting hero SKUs across Amazon, Flipkart and other marketplaces. The pricing team had no live view of the effective price customers actually paid once coupons, bank offers and EMI options were applied. They wanted more than monitoring. They wanted recommendations that learned from what the business accepted.

Our approach

How we approached it

  1. Mapped each of 20 hero products to 5 to 10 competitor SKUs across marketplaces
  2. Monitored prices, buy box and offers several times a day
  3. Computed the effective price after every discount layer
  4. Put recommendations in front of the team with accept and reject, then learned from every decision
Solution we created

What we built

A governed fleet of 14 agents and a 50 KPI pricing cockpit. Agents watch competitors, compute the real price, recommend cut, hold or raise by SKU and explain why. Dashboards link back to the ERP so finance sees margin impact.

Agents in the system

The multi agent team behind it

SourcesAmazon, Flipkart and other marketplaces, SAP, DMS, BI dashboards
SKU mapping agentLinks each product to its closest competitor matches
Price watch agentCaptures prices, buy box and offers across marketplaces
Effective price agentApplies coupons, bank offers and EMI to find the real price
Recommendation agentProposes price moves with the reasoning attached
Learning agentTunes future suggestions from every accept and reject
People decideReview, approve and act
SKU mapping agentLinks each product to its closest competitor matches
Price watch agentCaptures prices, buy box and offers across marketplaces
Effective price agentApplies coupons, bank offers and EMI to find the real price
Recommendation agentProposes price moves with the reasoning attached
Learning agentTunes future suggestions from every accept and reject
AmazonFlipkart and other marketplacesSAPDMSBI dashboards
Results

What changed in the numbers

15%uplift in eCommerce sales volume
70%of AI pricing suggestions accepted
30 to 40%efficiency boost for the pricing team

Measured in the client's live deployment.

Client names, detailed case studies, demos and references can be shared on request.

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