The Infrastructure Gap
Why Vector Databases Fail in Healthcare
You have the data. You have the AI models. But the infrastructure connecting them wasn't built for regulated industries. Vector RAG systems lack the core compliance and reasoning capabilities needed to automate high-value work like claim denials or clinical trial recruitment.
The Compliance Gap
Can't Prove How an Answer Was Found
To automate a prior authorization, you must prove the AI cross-referenced the specific insurance policy with the patient's lab results. Vector search can't create this auditable chain of evidence.
The Economic Gap
Too Expensive for Complex Queries
Finding eligible patients for a clinical trial requires querying thousands of unstructured records for multiple, specific criteria. The cost of running these complex queries on vector infrastructure is unsustainable.
The Trust Gap
Clinicians Can't Verify the 'Why'
To trust an AI-generated patient summary, a doctor needs to see the exact source note for every statement. Opaque vector similarity scores don't build the clinical trust required for adoption.
The Solution
The Compliant Infrastructure to Build On
Bagels provides the missing infrastructure layer. By transforming your unstructured data into a compliant knowledge graph, you can finally build the high-value AI applications that vector databases can't support—at a fraction of the cost.
Solve the Compliance Gap
Generate Verifiable Audit Trails
Our provenance-native architecture creates an unbreakable link between every piece of data and its source. Build AI that can prove its work to regulators, automatically.
Solve the Economic Gap
Run Complex Queries Cost-Effectively
Graph-native retrieval is surgically precise, fetching only the necessary data. This dramatically cuts token costs, making it economically viable to run complex queries across millions of documents.
Solve the Trust Gap
Build AI That Clinicians Actually Trust
Knowledge graphs provide clear, structured reasoning paths. Generate patient summaries or clinical recommendations that let doctors click back to the original source, building the trust needed for adoption.
How It Works
The Engine for Healthcare AI
Ingest Any Data
Connect PDFs, EMR records, and policies. We automatically structure messy healthcare data into a clean, compliant knowledge graph.
Query with Precision
Stop relying on fuzzy keyword matches. Ask complex clinical questions and get deterministic, evidence-backed answers.
Deploy with Confidence
Ship AI agents that cite their sources. Give clinicians the audit trails they need to trust the output.
Automate Operations
Streamline denial management and prior auth with verifiable AI reasoning.
Accelerate R&D
Find eligible patients for clinical trials in minutes, not months.
Build Trust
Give clinicians AI answers they can verify with a single click.
Ready to Build Auditable Healthcare AI?
Talk to our team about the auditable infrastructure you need to automate your most expensive operational bottlenecks and accelerate R&D.