Nearly 70% of B2B buyers struggle to find relevant products on supplier websites. Traditional keyword search fails on contextual queries about load capacity or contract terms.
Up to 27% of AI-generated responses in technical procurement contain factual inaccuracies when no grounding mechanism is applied. One wrong specification in industrial supply can trigger a safety incident.
92% - B2B buyers start with a vendor already in mind, one wrong price removes you permanently
Scenario 1
The assistant understands part number lookups, specification searches, RFQs, and order status queries from natural language. No SKU formatting or category navigation required.
Hybrid Search runs semantic and attribute-based lookup simultaneously. Data pulls live from Epicor P21, Adobe Commerce, Akeneo PIM, and the ingested knowledge base.
Every answer is verified against live data before reaching the buyer. Conflicting data is flagged for review, not surfaced.
Quote workflows run up to 70% faster. Verified pricing and availability feed directly into the conversation.
Buyer self-service adoption reaches 25–35% within the first month. Customer satisfaction scores reach 4.8 out of 5.
Rep call volume for routine pricing and availability queries drops significantly. Human teams focus on complex negotiations and high-value accounts.
Does it connect live to Epicor P21 ERP data?
Can it return exact SKU and part number matches?
Does it apply Guardrails to reduce hallucination?
Can it query video and training content directly?
Does it flag stale or conflicting product data?
Does it enforce account-specific pricing and roles?
Can it initiate RFQs and orders within chat?
Does it cite sources for every answer given?
Can it handle cross-reference and superseded parts?
Does it inherit approval chains from your ERP?