Product recommendations with AI Agents improve sales by 5%

Sigmoid helped a global B2B chocolate and cocoa products manufacturer achieve faster response to customer and higher portfolio utilization by implementing an Agentic AI-based product recommendation solution.

Business Scenario

The client is a global B2B manufacturer and supplier of chocolate and cocoa products with a diverse portfolio of over 20,000 SKUs and operations across 30+ countries. In its consultative sales model, responding to product inquiries from business customers necessitated significant collaboration between sales and R&D teams to determine the exact requirements. This process involved manually reviewing historical briefs, interpreting technical documents, identifying best-fit products from the portfolio and then crafting recommendations for each customer request. The process was slow, inconsistent, and difficult to scale due to a lack of structured knowledge reuse.

Sigmoid Solution

Sigmoid developed a Agentic AI-powered product recommendation engine that analyzes unstructured data from customer briefs and documents to provide accurate SKU recommendations. Built using multi-agent workflow, the engine autonomously orchestrated key tasks across the sales inquiry lifecycle including customer requirement analysis, product attributes extraction and mapping, recommending best-fit SKUs, and sales enablement with customized pitches and recommendations for cross-sell and upsell opportunities. A human-in-the-loop (HITL) mechanism allowed R&D and sales experts to validate outputs in real time, helping the system continuously learn.