Information Extraction

AI-powered extraction of structured data from unstructured text for better insights

Business impact

• Data Quality — Enhances accuracy and consistency of extracted information from raw text sources
• Information Retrieval Efficiency — Speeds up access to relevant data by structuring unstructured content effectively
• Operational Efficiency — Automates manual data processing, reducing time and resource consumption
• Knowledge Discovery — Surfaces latent insights by linking extracted entities into knowledge graphs

Data requirements

• Unstructured Text Documents (Text) — Primary input for extracting entities, key phrases, and semantic information
• Structured Databases (Structured) — Used to validate and enrich extracted information with existing records
• Images and PDFs (Image) — Processed via OCR to convert into text for subsequent extraction
• Knowledge Graphs (Structured) — Represent extracted entities and relationships for advanced querying and reasoning

AI methods and techniques

• Predictive AI — Used to identify and classify entities and relationships from text data
• Generative AI — Generates structured outputs and summaries from unstructured inputs
• Symbolic AI — Applies rule-based logic for entity linking and schema validation

AI models and model families

GPT-4o, GPT-4o-mini, BERT, PubMedBERT, Phi3, MiniLM-L6-v2

Industries

Real-world evidence

8 documented case studies on record.

Companies using this: AstraZeneca, Capital One, King College London, Kyndryl Deutschland Gmb H, Lex Rock AI Technologies Inc, Neo4j, Neo4j Inc, Regulatory Genome Development.