LLM-Based Structured Data Extraction

Extract structured, typed data from unstructured text using LLMs with strict schema validation, retry logic, and deterministic post-processing.

When to Use

• Building a pipeline that converts conversation/text into structured records
• Need deterministic extraction with provenance (every claim traces to source text)
• Implementing memory, knowledge graph, or data ingestion systems
• When extraction quality must be measurable across models

Core Pattern: Extract → Validate → Resolve

1. Extract: LLM produces structured candidates from input text
2. Validate: Deterministic gate checks provenance, schema, evidence
3. Resolve: Decision logic (ADD/MERGE/SUPERSEDE/NOOP) against existing records

Extractor Interface