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E-commerceAI implementation case study

E-commerce – Industrial MRO Marketplace: Seller & Buyer Trust Automation AI

AI for catalog normalization, counterfeit risk scoring, RFQ-to-order agents, and logistics exception handling.

This case study documents a real client implementation. Identifying details may be withheld where confidentiality applies.

Key takeaway

PrimeAxiom automated trust, matching, and fulfillment exceptions across a B2B marketplace with multi-agent coordination.

The engagement

From operational friction to a connected AI system

01 · Context

Business overview

Industry: industrial MRO marketplace. Complexity: long-tail SKUs, fraud risk.

02 · Challenge

Core problem

Manual catalog review, email RFQs, chaotic logistics.

03 · Approach

Solution

PrimeAxiom built marketplace operations AI: catalog cleansing bots, risk agents for suspect SKUs, RFQ parsing and routing, and carrier exception orchestration with seller penalties/incentives.

04 · Impact

Results

Catalog approval time −58%; dispute rate −37%; RFQ-to-order conversion +26%; ops headcount per GMV −19%.

System design

The full automation system

The implementation connects triggers, AI decisions, business rules, human approvals, and downstream actions in one controlled workflow.

Catalog Agent, Risk Agent, RFQ Agent, Logistics Exception Agent.

Workflow

How the automation runs

1. Seller upload. 2. AI normalizes attributes. 3. Risk score. 4. Buyer RFQ ingested. 5. Matching sellers notified. 6. Orders tracked; exceptions routed. 7. Seller score updates.

Intelligence layer

AI agents and decision logic

Normalize Agent, Fraud Agent, Matching Agent, Carrier Agent.

Under the hood

Technical architecture

Search + vector, rules for high-risk categories, LLM for RFQ parsing.

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