The foundation healthcare AI has been waiting for.
Serelora reduces liability, lowers costs, and increases accuracy in AI-driven healthcare by structuring patient data and agentic workflows.
No one reads the full chart
The physician has 15 minutes. The AI has a context window. The patient has 1,000 pages of medical records. Something always gets missed.
No physician can read 1,000 pages.
The average clinical encounter lasts less than fifteen minutes. Patient records routinely exceed 1,000 pages. No physician can read the full chart in the time they have, they have to guess what matters.
AI inherits the same problem and runs it at scale.
Clinical AI decides what to read, just like the physician scanning for what looks relevant. Then it infers connections across that selection. The output looks authoritative while the selection and inference remain invisible with no explanation of what it read, what it ignored, and why.
Missing context has a direct dollar cost.
Claims are denied because documentation doesn't demonstrate medical necessity. Inaccurate risk scores reduce reimbursement. Missed diagnoses create malpractice exposure. The physician guessed. The AI selected and inferred invisibly. The payer, the regulator, and the plaintiff do not accept either as an explanation.
"For these models, what matters is less whether a claim is correct than how it is written."
7.2%
of total revenue permanently lost to denials
73%
of physicians report AI tools do not give them confidence
$348K
average malpractice settlement for a missed diagnosis
$3K
revenue leakage from HCC documentation gaps per patient
Structure for clinical intelligence
Serelora reads the full chart so no one has to guess. We convert a patient's entire medical history into a single, structured clinical record that is instantly accessible and permanently traceable to its source. Every output can be verified. Every claim points back to the record that produced it.
That foundation changes what is possible across every workflow. Documentation becomes more accurate. Billing codes reflect the actual clinical evidence. Missed diagnoses and denied claims become the exception rather than the rule. Serelora is not another AI tool, it is the data infrastructure that makes every AI tool in healthcare trustworthy.
From fragmented records to executable workflows
Ingest
Patient uploads, clinical notes, claims data, intake forms. Any format. No interoperability hurdles.
HL7, FHIR, PDF, unstructured text
Structure
Records are organized in a knowledge graph where relationships between are made explicit.
Relationships, provenance, evidence
Act
AI agents query the graph to support clinical and administrative decisions, with full source traceability.
Scribe, risk scoring, coding, CDS
Built for the one environment
where AI cannot be wrong silently.
Healthcare is the highest-stakes reasoning environment in existence. Every Serelora output is linked to evidence, approved by a clinician, and traceable to source.
Provenance on every data point
Every recommendation links back to the exact chart entry and supporting medical literature that generated it. Clinicians can audit the full reasoning chain, not just the conclusion.
Human approval gates all actions
AI agents surface recommendations and draft outputs. Clinicians approve before anything executes. The system is designed to augment clinical judgment — it never replaces it or acts unilaterally.
Failure isolation by design
When one agent fails or lacks sufficient data, it surfaces uncertainty rather than producing a confidently wrong answer. The architecture is built to expose gaps, not paper over them.
Reasoning trace
Action surfaces to clinician
Conclusion
Escalation risk: Heart Failure
Confidence: High · Agent: Risk scoring
↓ grounded in
Clinical note · Jan 27
"Patient reports orthopnea over the last 48 hours."
Scribe note · attending Dr. Reyes
+ corroborated by
Claims data · Jan 20
ICD-10-CM I50.9 — Heart failure, unspecified
HCC 85 · Risk weight: 0.331
+ supported by
Literature — NEJM 2024
Orthopnea predicts BNP elevation and decompensation in CHF patients with 78% specificity.
If we can't explain how an answer was reached, it doesn't belong in healthcare.
Spencer Wozniak
Co-Founder
One source of truth. Multiple layers of value.
The same structured patient data drives better decisions for clinicians, stronger economics for administrators, and more complete care for patients.
Value
Reduced liability, clearer decisions, and more time with patients.
How We Deliver
The full patient record is structured and accessible without summarization, with every conclusion linked to its source so clinicians make decisions with complete context, not partial views.