Healthcare runs on documents.
Most of them are unreadable by AI.
Clinical PDFs, insurance forms, discharge summaries. Dense, nested, full of implicit references. Generic AI tools hallucinate. We build systems that actually work.
The problem isn't AI.
It's how healthcare documents are built.
50-83%
Hallucination rates in general-purpose LLMs on clinical documents
80%
Of healthcare data is unstructured and locked in PDFs
$42B
Annual cost of medical errors linked to documentation failures
Why generic AI tools fail on clinical documents
- 1Nested tables and multi-column layouts break standard parsers
- 2Implicit references across sections require domain context
- 3Regulatory terminology needs specialized entity recognition
- 4Multi-format documents (scans, forms, notes) demand hybrid pipelines
Our Pipeline
We build document intelligence systems for healthcare
End-to-end solutions from raw clinical documents to verified, auditable AI outputs.
Clinical Document Parsing
Extract structured data from complex healthcare documents including discharge summaries, lab reports, and insurance forms.
Semantic Chunking
Intelligent document segmentation that preserves clinical context and relationships between sections.
Entity Linking
Map extracted entities to medical ontologies (SNOMED, ICD-10, RxNorm) for standardized representation.
Knowledge Graph RAG
Graph-based retrieval that understands relationships between conditions, medications, and procedures.
Hallucination Detection
Multi-layer verification systems that catch fabricated information before it reaches end users.
Audit-Ready Outputs
Every AI response includes source citations, confidence scores, and full retrieval traces.
Let's talk about your documents
Schedule a call to discuss your healthcare document challenges. We will explore whether our approach fits your needs.