Santorio
From Months to Same-Day
AI-Orchestrated Hematologic Diagnostics
Turnaround Revolution
Transforming weeks-long processes into <24h diagnostic delivery
The Bottleneck in Hematologic Diagnostics
Manual bone marrow morphology review is the gating step delaying therapy decisions.
Operational Reality
Turnaround Crisis
2–6 weeks current timeline with potential <24h automation capability
Expert Throughput Limits
~6–10 samples per cytologist per day maximum capacity
Treatment Impact
Therapy initiation delayed 10–21 days due to diagnostic bottlenecks
Labor Inefficiency
40–60% expert time consumed on routine morphology triage
Process Friction Points
Why current workflows fail to scale
Capacity Ceiling
Human review doesn't scale with monitoring demand
Serial Manual Steps
No pre-filter; every frame enters expert queue
Variable Consistency
Inter-observer variance reduces confidence
Compliance Overhead
Post-hoc traceability assembly = slow + brittle
Opportunity: Deterministic, image-heavy workflow is automation-ready—unlocking speed, consistency, and regulatory-grade lineage.
Platform Overview
End-to-end modular workflow acceleration with embedded compliance.
Versioned Inference Registry
Complete model version control and deployment tracking
Dataset ↔ Model Linkage
Immutable connections between training data and model outputs
Audit-Grade Lineage Graph
Complete traceability for regulatory compliance
Clinical & Operational Impact
Before (Manual)
Turnaround
Weeks
Per-Sample Expert Time
~2.5 hours
Consistency
Variable
Traceability
Assembled after the fact
After (Santorio Trajectory)
Turnaround
<24h (goal)
Expert Time
<30 min (AI triage + assisted review)
Consistency
Standardized, versioned models
Traceability
Built-in lineage spine
3-5×
Productivity Gain
Potential diagnostic throughput per site
Architecture & Defensibility
Layered System
01
Ingestion / Capture
UScan module for high-throughput sample processing
02
Assisted Labeling & Active Learning
Annotation module with continuous model improvement
03
Orchestrated Inference
Model gateway with asynchronous task processing
04
Governance Spine
Lineage graph, version registry, audit ledger
Key Advantages
Built-in competitive moats
Model-Agnostic Architecture
Plug-in architecture allows for flexible model integration and future upgrades without system overhaul
Microservices Separation
Independent training, inference, and orchestration services enable scalable and maintainable deployment
Immutable Data Binding
Permanent data→model→output connections ensure complete traceability and regulatory compliance
Data Flywheel Effect
Every site generates more labeled variance, driving continuous accuracy improvements across the network
Structural moat: compliance-grade lineage built early.
Data Flywheel & Traceability
Deployed Sites
Clinical installations generating real-world data
Diverse Frames
Varied sample types and conditions
Assisted Annotation
Expert-guided labeling with AI assistance
Active Learning Retraining
Continuous model improvement cycles
Accuracy Uplift
Enhanced diagnostic performance
Faster Adoption
Improved results drive market expansion
Traceability Spine
Immutable Lineage
Complete sample → annotation → model → inference response tracking
Version Registry
Dataset & model hash binding for complete version control
Audit API
Instant retrieval capabilities for regulatory & QA review
Outcomes
- Lower approval friction
- Faster model iteration cycles
- Trust & reproducibility baked-in
Contact
santorio@datafund.io