Extract regulatory data from complex source documents
Extract high-value regulatory data from SmPCs, RIM records and other complex source documents
Overview
AI Extraction helps teams capture structured regulatory data from heterogeneous sources faster, with less manual effort and better consistency across products, packages, and manufacturers
Multi-source Ingestion
Capture data from SmPCs, legacy documents, spreadsheets, and RIM records across different formats and structures.
Entity Extraction
Identify key regulatory entities such as products, pack sizes, manufacturers, strengths, and key metadata fields.
Human Review
Support validation and correction by routing extracted data for review before downstream mapping or submission preparation
Example Code for
AI Extraction
See how Theranext turns complex regulatory data into structured, validated, and operational outputs through configurable workflows across extraction, integration, and readiness activities.
# Example extraction flow
source = "SmPC / RIM / legacy document"
extract_entities = ["product", "strength", "manufacturer", "pack size"]
normalize_output = "structured regulatory dataset"
review_mode = "human-in-the-loop"
target = "IDMP mapping / RIM / MDM"
Move from complexity to execution
Theranext helps pharma teams structure data, deliver transformations, and operationalize AI across R&D processes.
