Cellythra

The Cellythra pipeline.

A privacy-first architecture for regenerative health intelligence. From raw biomedical and psychological data to clinician-supervised care pathways — built for European healthcare providers.


From raw data to regenerative insight.

Step 01

Multimodal Intake

Lab reports, medical documents, biomarkers, lifestyle and psychological data — captured in one place.

90+

Step 02

Clinical Normalization

90+ biomarkers structured against validated medical reference ranges.

Step 03

AI-Assisted Pattern Recognition

Contextual patterns and regenerative signals surfaced across time — for a clinician to interpret.

Human checkpoint

Step 04

Clinician Review

Every AI-surfaced signal is interpreted and verified by a clinician before it reaches a care decision.

Step 05

Clinician-Guided Care Pathways

Personalised, clinician-supervised health trajectories — decided by the clinic, informed by Cellythra.

A four-layer architecture for trusted intelligence.

Layer 1

Encrypted Data Ingestion

Encrypted capture from PDFs, lab reports, structured inputs and patient-reported outcomes.

Layer 2

Biomedical Structuring Layer

Every marker mapped against clinically validated ranges before it ever reaches an insight.

Layer 3

Clinically-Grounded Intelligence (RAG)

Retrieval-augmented generation, grounded and traceable to clinician-reviewed medical knowledge.

Layer 4

Clinical & Patient Interface

A clinician-facing surface for longitudinal review, and a patient-facing experience alongside it.

Privacy-first by architecture, not by promise.

Every Cellythra layer is designed for European healthcare providers. Data is encrypted at rest, hosted in Europe, and never used for advertising, resale or model training.

End-to-end encryption

AES-256 at rest, TLS 1.3 in transit. Keys managed per clinic.

European data residency

All patient data hosted exclusively in EU data centers. No third-country transfers.

No advertising, no resale

Patient data is never sold, brokered or used for advertising or tracking.

No model training on patient data

AI assistance is grounded in clinician-reviewed knowledge — never trained on patient records.