Three Cutting-Edge Technologies.
One Intelligent System.
PolicyGraph combines semantic web, causal inference, and advanced
NLP to understand insurance documents like never before.
The Technology Stack
Why we use technologies most insurance platforms don't
Semantic Web (RDF/OWL)
What it is: W3C standard for representing knowledge as interconnected graphs, not tables.
Why we use it: Insurance isn't rows and columns. It's relationships. "Coverage HAS benefits UNLESS exceptions apply."
Benefit: True understanding, not just text search. We model how insurance actually works.
Causal Inference
What it is: Mathematical framework for modeling cause-effect relationships and conditional logic.
Why we use it: Insurance is all "IF death THEN payout UNLESS suicide within 13 months." Traditional databases can't model this.
Benefit: Answer "what if?" questions accurately. Handle complex conditional logic.
Advanced NLP
What it is: Natural language processing with conditional logic extraction and BM25 ranking.
Why we use it: Extract structure and conditions from natural language. No manual data entry.
Benefit: Automatically process new policies. Scale infinitely.
What PolicyGraph "Sees"
While other systems see text, we see structure and logic
Knowledge Graph Visualization
Coverage
$500,000
This graph representation enables "what if" queries that traditional databases cannot answer
How It Works: The Pipeline
1. Document Ingestion
Upload policy PDFs or connect to document management systems. We handle any format.
2. Structure Extraction
NLP extracts headers, sections, tables, and hierarchies. Builds document structure automatically.
3. Entity Recognition
Identifies coverages, benefits, exclusions, waiting periods, dollar amounts, and conditions.
4. Conditional Logic Extraction
Extracts "unless," "provided," "if" clauses. Builds causal rule graphs. This is our secret sauce.
5. Knowledge Graph Construction
Creates RDF triples linking entities and relationships. Enriches with content analysis (readability, complexity, etc.).
6. Search Index Building
Builds BM25 index for text search. Combines with graph queries for hybrid retrieval.
7. Query & Reasoning
Answer questions using: BM25 text search + SPARQL graph queries + causal reasoning. Get instant, accurate answers with full provenance.
PolicyGraph vs Traditional Approaches
Built on Research
PolicyGraph combines academic research with practical engineering
Semantic Web Standards
Built on W3C RDF, OWL, and SPARQL standards. Interoperable, extensible, future-proof.
Standards: RDF 1.1, OWL 2, SPARQL 1.1
Causal Inference Theory
Based on Pearl's causal hierarchy and do-calculus for modeling interventions and counterfactuals.
Research: Causal graphs, SCM models
Information Retrieval
BM25 ranking algorithm (Okapi BM25) proven superior to TF-IDF for document retrieval.
Research: Robertson & Zaragoza, 2009
Natural Language Processing
Dependency parsing, named entity recognition, and relation extraction from unstructured text.
Tools: spaCy, custom insurance NER
Readability Science
Flesch-Kincaid readability formulas validated across millions of documents.
Metrics: Reading ease, grade level
Ongoing Research
Partnerships with universities on insurance document understanding and causal reasoning.
Publications: Conference papers in progress
See the Technology in Action
Experience how semantic web + causal inference solves insurance complexity
45-minute deep-dive with our CTO • Architecture walkthrough • Q&A