AI agents power efficiency gains in MedTech R&D

A global MedTech leader partnered with Sigmoid to accelerate product development by improving access to critical R&D knowledge buried in fragmented documents and systems.

Business Scenario

A leading global MedTech company specializing in manufacturing diagnostic devices and surgical instruments was facing inefficiencies in its R&D operations. Critical product information was scattered across unstructured Design History Files (DHF) and SAP systems, making it difficult for R&D teams to locate relevant data quickly and reliably. To address this, the company initiated an AI-led transformation of its R&D processes, aimed at significantly improving efficiency, accuracy, and knowledge access across teams.

Sigmoid Solution

Sigmoid developed an Agentic AI platform that integrated structured SAP data and DHF documentation to create a unified, searchable knowledge repository. A domain-specific Q&A agent framework was built using Open AI and other LLM enablement frameworks for vector embedding, indexing etc., supported by a comprehensive Azure ecosystem—including DevOps, Monitor, Repos, and other services. The solution combined a robust data ingestion pipeline from SAP Everest and Tahiti systems with a modern unstructured data mesh capable of linking metadata and document content across product lines. This agentic framework interpreted natural language queries from R&D teams, delivering highly accurate, context-rich responses with over 95% precision.