AI Infrastructure
Layered AI for organizations with high-stakes data.
We separate the parts of an AI system that should change from the parts that shouldn't. Foundation models replace. Domain knowledge, your data, your tooling, and the apps people use compound.
What we build
Three things, designed to fit together.
Synthetic Data Generation
Clinical-grade synthetic patient records validated against real-world distributions. For research, model training, and regulatory submissions.
Custom Clinical AI
Private clinical AI trained on your de-identified data, deployed on-premise with full data sovereignty. For hospitals, medical groups, and labs.
Foundation Models
MedBase clinical foundation models from 7B to 400B+ parameters, plus customization layers for open-source and licensed bases via continued pretraining, fine-tuning, and adapters. ModelForge-powered.
Our infrastructure
The tools we use to deliver models to our customers.
Cortex turns published research into structured knowledge. ModelForge produces RonanLabs' MedBase clinical foundation models and the customization layers — continued pretraining, fine-tunes, and adapters — that adapt any L0 base to your domain. Nexus is the knowledge graph that links them. None of these are generic — they are the internal infrastructure that makes our customer engagements compound.
Infrastructure
Cortex
Research intelligence platform — turns published literature into structured, queryable knowledge.
Learn how it works →
Infrastructure
ModelForge
Model training factory — turns customer data into deployment-ready custom models.
Learn how it works →
Infrastructure
Nexus
Knowledge graph — the shared substrate that links domain, profession, and customer layers.
Learn how it works →
Architecture
Six layers. Clear ownership at each.
From the replaceable base model to the app surface a person actually touches. Each layer is owned by whoever is best positioned to own it.
Where we deploy
Built for regulated industries.
We work in domains where the data is high-stakes, the constraints are real, and the off-the-shelf options don't fit.
- Hospitals & Health Systems
- Medical Groups & Specialty Clinics
- Life Sciences (Pharma, Biotech, CROs)
- Diagnostic & Pathology Labs
- Insurance & Underwriting
- Banking & Financial Services
- Legal — Corporate In-House
- Legal — Outside Firms
- Construction & Skilled Trades
- Industrial Operations (HVAC, Refrigeration, Manufacturing)
- Aviation
- Professional Societies & Accreditation Bodies
- Government & Defense
- Education & Training Organizations
- Energy & Utilities
Research
Published findings.
Methodology
Hybrid Pipeline Achieves 94% TSTR Fidelity
Combining structural generation with GAN correction and LLM enrichment produces synthetic data indistinguishable from real clinical records in downstream ML tasks.
Validation Framework
6-Layer Automated Validation for Synthetic Clinical Data
A comprehensive quality framework spanning statistical fidelity, clinical pathway accuracy, temporal consistency, TSTR utility, NLP coherence, and differential privacy guarantees.
Benchmark Study
Why Raw Synthetic Data Fails Clinical AI
Commodity synthetic generators score 65-75% on Train-Synthetic-Test-Real benchmarks. We quantify the gap across clinical domains and demonstrate how hybrid correction closes it.
White Paper
On-Premise Clinical AI Without Data Exposure
Architecture and methodology for training custom hospital AI on de-identified data while maintaining full data sovereignty and HIPAA compliance.
Tell us about your data and your constraints.
We will tell you what is and isn't possible. No pitch deck.
Start the conversation