The intelligence layer

AI you can put in front of an auditor.

Most healthcare “AI” points a language model at a chart and hands you a summary. Cascala is built the opposite way: a clinical intelligence layer that reasons above the source documents, cites every claim back to the record, and produces a verifiable, audit defensible trail. Every protocol is written and reviewed by our clinical team.

Source-cited by default

Verifiable, audit defensible

Configurable clinical knowledge bases

Built & reviewed by clinicians

A clinical intelligence layer that lives above the record.

Summarization tells you what a document says. Cascala connects information across the record to surface what matters, what’s at risk, and what to do next.

Reasoning across sources
Connects diagnoses, medications, labs, and transition signals to identify risks and gaps—not just restate notes.

Clinical knowledge, applied
Turns source documentation into clear, patient-specific insights and next steps.

Clinician-authored logic
Built on protocols developed and reviewed by clinicians and our medical advisory board.

How Cascala helps

Every claim traces back to the source.

In risk-bearing and post-acute settings, an insight you can’t defend is a liability. Cascala is built so everything it surfaces carries its evidence with it — ready for a coder, a clinician, or an auditor.

Source-cited extractions

Every insight links to the exact document and location — e.g. ‘re-admit risk: High — source: Discharge Summary p.3.’ Nothing is asserted without a citation.

Audit-defensible documentation

Suspected conditions and HCCs surface with the source evidence attached, supporting clean, auditable coding that stands up to audit review.

Built for the latest requirements

Extractions and recapture logic align to the current risk-adjustment model — defensible at the moment it’s created, not reconstructed later.

A record across every setting

The audit trail follows the patient across hospital, SNF, home health, and PCP — one reconcilable evidence base for shared savings and star ratings.

Your standards of care, wired in.

The clinical knowledge layer is configurable. Cascala can reason against the evidence sources your clinicians already trust — so outputs reflect your standard of care, not a generic model’s.

Clinical evidence base
Recommendations reflect current, cited clinical literature.

Physician reviewed protocols
Rigorous approach to ensuring all workflows actually match how care is delivered.

Client-configurable
Bring the knowledge bases and protocols your clinical leadership chooses — the layer adapts to your standard, and every output stays cited to its evidence.

98%

clinical extraction accuracy

observed critical errors or hallucinations in >250k unique extractions

of extractions flagged in human review — none critical

Accuracy we measure, not accuracy we claim.

In human review, only 2% of extractions were flagged for potential improvement — none involving critical clinical errors or fabricated clinical information. Because every extraction is source-cited, a reviewer can verify any claim against the original record in seconds.

Human-in-the-loop review
Extractions are reviewed against the source, so accuracy is measured against ground truth — not self-reported by the model.

No fabricated clinical information
Across reviewed extractions, zero critical clinical errors and zero hallucinated clinical facts were observed.

Verifiable, not just accurate
Source citations make every output checkable — the accuracy figure is auditable, not a marketing number.

Responsible by design

This is not an AI chatbot pointed at a patient record.

Every protocol was written by our clinical team. Every extraction is traceable to its source. Our medical advisory board — medical directors from UNC Health, Equality Health, Oak Street, and beyond — reviews the logic that governs what Cascala surfaces. That’s the difference between an AI experiment and clinical intelligence you can stand behind.