Nearly seven in ten B2B buyers struggle to find relevant products once they land on a supplier site, especially with large catalogs.
In catalogs with thousands of SKUs, a significant share of buyers abandon before completing orders when they cannot build carts efficiently.
Manual bulk order handling across spreadsheets, email, and ERP drives delays, errors, and higher service costs for distributors.
Scenario 1
Buyer uploads a file, pastes SKUs, imports from history, or describes needs in natural language. The assistant parses SKUs, quantities, and any free form constraints in one flow.
AI validates SKUs, resolves unknown codes, and suggests alternatives from the catalog. It checks ERP for pricing, MOQs, credit rules, and multi warehouse availability.
Assistant returns a ready to submit cart or quote, highlighting issues and options. It supports approvals and RFQ flows, writing back into ERP and ecommerce with clean data.
Faster quote and order cycles as AI handles initial validation instead of humans.
Fewer pricing and SKU errors leading to rework, credits, or returns.
Higher completion rates on large carts when buyers can self serve with confidence.
Can it accept multiple input methods, including file uploads, pasted SKUs, and order history?
Does it validate SKUs against your full catalog and suggest alternatives for invalid codes?
How does it connect to ERP for pricing, MOQs, and multi warehouse inventory?
Can it support both self service ordering and sales assisted workflows for key accounts?
Does it align with existing approval chains, CPQ rules, and RFQ processes?
How will it learn from exceptions and errors to reduce manual fix work over time?
What is a realistic pilot and rollout timeline for your current ERP and ecommerce stack?