ProcDNA developed a scalable, GenAI-powered solution that converts unstructured documents into a structured, queryable knowledge base, enabling business users to access insights via natural language.
Ingests raw PDF and DOCX files and applies a strict JSON schema to guide LLM-based extraction of domain-specific attributes.
Acts as a domain-specific NLP agent to extract key entities with enforced formatting and high schema compliance.
Transforms complex hierarchical outputs into flat, analysis-ready datasets and consolidates them into a centralized Single Source of Truth.
Enables tailored chatbots for functions such as Medical Affairs and Market Access.
Automatically converts business questions into executable SQL queries for self-serve analytics.
Provides both interactive data tables and natural language summaries for faster decision-making.
Differentiates conversational and analytical queries to optimize performance and response accuracy.
Supports concurrent users and conversational history for seamless follow-up queries.
Reduced insight discovery from days of manual review to near real-time automated extraction.
Ensured standardized, error-free structured outputs across documents, creating a trusted data foundation.
Cut turnaround time for complex business queries from days (via IT tickets) to seconds through self-serve natural language queries.
Proven capability to ingest, flatten, and deduplicate high volumes of diverse file formats, enabling enterprise-scale deployment.
