Research
Information Extraction
Turning messy records into facts, links, and evidence.
Information extraction is the bridge between raw records and useful operating models. The goal is not just to extract text; it is to preserve the entity, relationship, source, and confidence needed to act on it.
Core Questions
Which entities and relationships matter for finance, compliance, customer work, and follow-up?
How should extraction systems expose confidence, ambiguity, and missing evidence?
When should extraction use schemas, graphs, language models, rules, or human review?
Artifacts
InfoExtract utilities.
GraphForge graph construction experiments.
Community reads on relation extraction and text-to-graph methods.
What It Means
Where It Shows Up
Structured Records
Turn invoices, contracts, statements, emails, and notes into structured records.
Evidence Links
Attach source spans and evidence links to extracted facts.
Reviewable Inputs
Create reviewable inputs for planning, compliance, and advisory workflows.
Why It Matters
How This Research Gets Used
Applied
Product direction
Research themes shape product workflows, internal evaluation, and open-source implementation choices.Evidence
Reviewable decisions
The work emphasizes assumptions, provenance, and feedback loops that humans can inspect.Browse Research
Turning messy records into facts, links, and evidence.