A Clinical Research Organisation (CRO) managing large volumes of clinical and study documentation. Its teams review patient records, clinical reports, spreadsheets and tracking documents, correlate patient and study identifiers across sources, and compile the findings into predefined report formats.
Critical information in clinical workflows sits across reports, patient records, spreadsheets and tracking documents. Finding an answer meant manually reviewing multiple files, correlating patient or study identifiers, interpreting narrative content and copying the findings into fixed report formats. Keyword search could not help, because the same information is worded differently across documents and often depends on its surrounding context.
Large-Scale Unstructured Data
Documents run to hundreds of pages of narrative and tabular content, making manual search harder as volumes grow.
Contextual Retrieval
Relevant content rarely matches the exact words in a query, so the system had to understand meaning, not just keywords.
Cross-Document Correlation
Answers were spread across sources and needed patient, site and subject identifiers to be linked before a response could be built.
Maintaining Source Context
A few matching passages gave incomplete answers. The right surrounding context and chunk combinations had to reach the AI.
Scaling Beyond Q&A
The goal was a platform that reasons over documents, retrieves what it needs, validates context and generates structured outputs, not a basic document chatbot.
Pace Wisdom built a RAG-based clinical document intelligence platform that combines custom document processing and indexing with AI-powered retrieval and generation. Key capabilities include:
Semantic Indexing and Search
Documents are segmented into meaningful chunks, enriched with metadata and indexed as vector embeddings, so queries match on meaning even when the wording differs.
AI-Assisted, Query-Aware Retrieval
The platform interprets the intent of each request and uses AI to select the chunks that matter, improving the context passed to the generation layer.
Cross-Document Correlation
Information from multiple sources is linked through shared identifiers and context before the final response is generated.
Template-Driven Generation
Users supply report templates and instructions, and the platform produces outputs that follow the required reporting format.
Agentic Workflow for Narrative Documents
An agent follows a loop of Understand, Plan, Retrieve, Evaluate, Correlate, Refine, Generate and Validate. It re-retrieves when context is missing and checks the response against the evidence before presenting it.
A Clinical Research Organisation (CRO) managing large volumes of clinical and study documentation. Its teams review patient records, clinical reports, spreadsheets and tracking documents, correlate patient and study identifiers across sources, and compile the findings into predefined report formats.
Critical information in clinical workflows sits across reports, patient records, spreadsheets and tracking documents. Finding an answer meant manually reviewing multiple files, correlating patient or study identifiers, interpreting narrative content and copying the findings into fixed report formats. Keyword search could not help, because the same information is worded differently across documents and often depends on its surrounding context.
A Clinical Research Organisation (CRO) managing large volumes of clinical and study documentation. Its teams review patient records, clinical reports, spreadsheets and tracking documents, correlate patient and study identifiers across sources, and compile the findings into predefined report formats.
Critical information in clinical workflows sits across reports, patient records, spreadsheets and tracking documents. Finding an answer meant manually reviewing multiple files, correlating patient or study identifiers, interpreting narrative content and copying the findings into fixed report formats. Keyword search could not help, because the same information is worded differently across documents and often depends on its surrounding context.
Large-Scale Unstructured Data
Documents run to hundreds of pages of narrative and tabular content, making manual search harder as volumes grow.
Contextual Retrieval
Relevant content rarely matches the exact words in a query, so the system had to understand meaning, not just keywords.
Cross-Document Correlation
Answers were spread across sources and needed patient, site and subject identifiers to be linked before a response could be built.
Maintaining Source Context
A few matching passages gave incomplete answers. The right surrounding context and chunk combinations had to reach the AI.
Scaling Beyond Q&A
The goal was a platform that reasons over documents, retrieves what it needs, validates context and generates structured outputs, not a basic document chatbot.
Pace Wisdom built a RAG-based clinical document intelligence platform that combines custom document processing and indexing with AI-powered retrieval and generation. Key capabilities include:
Semantic Indexing and Search
Documents are segmented into meaningful chunks, enriched with metadata and indexed as vector embeddings, so queries match on meaning even when the wording differs.
AI-Assisted, Query-Aware Retrieval
The platform interprets the intent of each request and uses AI to select the chunks that matter, improving the context passed to the generation layer.
Cross-Document Correlation
Information from multiple sources is linked through shared identifiers and context before the final response is generated.
Template-Driven Generation
Users supply report templates and instructions, and the platform produces outputs that follow the required reporting format.
Agentic Workflow for Narrative Documents
An agent follows a loop of Understand, Plan, Retrieve, Evaluate, Correlate, Refine, Generate and Validate. It re-retrieves when context is missing and checks the response against the evidence before presenting it.
