PDI Technologies has unveiled PDI Intelligence Query (PDIQ), an internal AI assistant designed to unify scattered corporate knowledge bases into a searchable chat interface. By leveraging a custom Retrieval-augmented generation (RAG) system, the platform allows employees to query data across disparate sources like SharePoint and Confluence with ease. This isn't just a simple search tool; it's a robust infrastructure built on AWS that handles complex data ingestion and security at scale. The technical core of PDIQ lies in its unique approach to processing information. Rather than just breaking documents into random pieces, the system generates a concise summary of the document and attaches it to every single fragment. This ensures that even if the AI only looks at one small part of a manual, it understands the overall context. This specific innovation helped PDI increase their response accuracy from 60% to 79%, demonstrating how smart data preparation is often more important than the choice of Foundation Model. Furthermore, PDIQ uses vision-capable models to describe images within documents. These captions are turned into searchable text, meaning diagrams are just as findable as written text. By integrating these insights, PDI has created a blueprint for how enterprises can safely deploy Large language model (LLM) technology to solve productivity bottlenecks.

PDI Technologies has unveiled PDI Intelligence Query (PDIQ), an internal AI assistant designed to unify scattered corporate knowledge bases into a searchable chat interface. By leveraging a custom Retrieval-augmented generation (RAG) system, the platform allows employees to query data across disparate sources like SharePoint and Confluence with ease. This isn't just a simple search tool; it's a robust infrastructure built on AWS that handles complex data ingestion and security at scale. The technical core of PDIQ lies in its unique approach to processing information. Rather than just breaking documents into random pieces, the system generates a concise summary of the document and attaches it to every single fragment. This ensures that even if the AI only looks at one small part of a manual, it understands the overall context. This specific innovation helped PDI increase their response accuracy from 60% to 79%, demonstrating how smart data preparation is often more important than the choice of Foundation Model. Furthermore, PDIQ uses vision-capable models to describe images within documents. These captions are turned into searchable text, meaning diagrams are just as findable as written text. By integrating these insights, PDI has created a blueprint for how enterprises can safely deploy Large language model (LLM) technology to solve productivity bottlenecks.