The ground truth for the decade of AI
5B+ records across 70M+ patients. Coded, regulatory-grade ground truth, supplied in under 72 hours.
Refinery online · availableThe decade of AI runs on health data
And the data is the bottleneck.
Models are bottlenecked by data.
Model capability depends on the data beneath it, not on compute. What the field is short of is high-fidelity, coded, longitudinal clinical data.
Medicine is going in‑silico.
Digital twins, synthetic control arms, and in-silico trials now run through discovery, development, and approval, and regulators increasingly accept real-world evidence. Every one of them depends on deep, trustworthy clinical data.
Real‑world data is being redefined.
The standard has moved past shallow legacy records. What counts now is refined, coded, longitudinal data that is multimodal and machine-readable, and it's exactly what both frontiers are running out of.
Two frontiers. One bottleneck. One source of ground truth.
We own it.We refine it.We supply it.
We own 5B+ records, we refine them into coded ground truth, and we supply them in under 72 hours. The whole ecosystem, end to end.
Our proprietary, multimodal corpus, held by us, not processed for others.
- Encounters
- Diagnoses
- Problem lists
- Procedures
- Medications
- Immunizations
- Allergies
- Lab results
- Vital signs
- Clinician notes
- Care plans & goals
- Social history
- Family history
- Claims
- Coverage
Coded · longitudinal · mapped to your needs.
Ground truth, ready to run.
Coded clinical datasets for analytics & training.
Train and test in our world.
RL environments & harnesses, built to your objective.
Your population, precisely cut.
Criteria-matched patient cohorts.
Measure what's real.
Private, leakage-controlled benchmarks.
How medicine actually reasons.
Verifiable longitudinal decision chains.
Decision-ready evidence.
Analytics-ready real-world evidence.
One record.We have millions.
Health is never static.
One record, coded end to end · We own 70M+ patients · 5B+ records
Built for your objective.
For Model Development
Pre-training
A base model that reasons in clinical structure, not just words.
- Medical foundation models: clinical reasoning baked into the base; coded + narrative + structure in one window is the alignment signal SFT can't retrofit.
- Clinical world-models: predict trajectory, not just answer questions; decade-long arcs are the temporal substrate.
- Substrate-level grounding: native SNOMED/ICD/RxNorm cross-walks kill drug and concept hallucination before fine-tuning starts.
Domain adaptation
Medical fluency, layered onto your current model.
- Domain-adaptive mid-training: inject medicine into a general base (Grok/Llama-class) without a from-scratch run.
- Vertical clinical model lines: health-specific variants for enterprise and clinician-facing products.
- Legacy-robust models: handle ICD-9↔ICD-10 and multi-vendor formats because the corpus spans the transition.
Post-training (SFT)
Models that code, extract, and summarize from real records.
- Autonomous medical coding: text → ICD/SNOMED/CPT/RxNorm; parallel-coded pairs are native instruction data.
- Ambient documentation: note generation grounded in real clinician dictation, not templates.
- Structured extraction: pull problems, meds, and labs from notes; typed-absence teaches negation ("denies chest pain").
RL & reasoning
An RL environment where "what happened next" is ground truth.
- Verifiable reasoning (RLVR): reward multi-step clinical reasoning against a checkable real outcome.
- Agentic clinical workflows: agents that order, follow up, and titrate; the care graph is the action space.
- Outcome-conditioned policies: learn standard-of-care from what clinicians did and what actually resulted.
Evals & environments
A private eval and harness, not an off-the-shelf benchmark.
- Private clinical benchmarks: measure on held-out real data the model hasn't memorized; public benchmarks are saturated.
- Trajectory-prediction evals: test world-model quality, not just Q&A accuracy.
- Safety harnesses: drug-interaction, negation, and calibration tests built from the same ground truth.
The clinical ground truth your models reason from.
For Drug Development
Market sizing & epidemiology
Prevalence, incidence, and treated population by indication.
- Addressable-population sizing: diagnosed + treated counts by indication and line of therapy.
- Burden-of-illness & epidemiology: incidence/prevalence trends and comorbidity profiles.
- Sub-population targeting: find the treatable segment inside a broad diagnosis.
Patient journey & treatment pathways
Lines of therapy, switches, and time-to-event.
- Treatment-sequence analysis: what's prescribed first, second, and when patients switch.
- Adherence & persistence: start/stop/modify signal across the full timeline.
- Treatment-gap mapping: where real care deviates from guideline, the white space for a brand.
Trial feasibility & cohort discovery
Cohort counts, criteria sensitivity, and site maps.
- Protocol feasibility: does enough of the population meet I/E? Tighten criteria against real counts.
- Site & investigator selection: where eligible patients actually are.
- RWE cohort definition: specify the analysis population precisely with multimodal criteria.
External / synthetic control arms
Matched control arms: covariate-balanced, time-aligned outcomes.
- External control arms: regulatory-grade comparators for single-arm oncology and rare-disease trials.
- Synthetic control arms / digital twins: expected outcomes modeled from real longitudinal histories.
- Comparative effectiveness: real-world head-to-head outcomes with covariate adjustment.
Label expansion
Real-world effectiveness and safety evidence, regulatory-ready.
- New-indication evidence: real-world effectiveness for an expanded population.
- Post-market safety: pharmacovigilance signal detection across the trajectory.
- HEOR & access dossiers: real-world value evidence for payers and reimbursement.
Multimodal real-world data for your therapy areas, refined to your indication and streamed into your analytics. One source, every modality.
72 hours, not months.
Data infrastructure built for the decade of AI, at scale, not just for today.
Commitment to trusted ground truth
Certified, compliant deidentification
The best clinical data is worthless if your legal team won't let it near your training run. Every delivery arrives fully deidentified with its compliance artifacts in hand, so you ingest into your main training or analytics environment on day one, with no BAA, no IRB, no per deployment review.
Clean room delivery
Take delivery inside your own infrastructure: cloud, VPC, on prem, or secure enclave. You keep full control of the data and the environment; nothing crosses your perimeter, and there's no external risk surface.
Forward deployed partnership
Our team works beside you across the full lifecycle: scoping your objective, refining the corpus into the exact dataset, cohort, or environment you need, and delivering production ready artifacts that meet real world requirements.
The real world, encoded.
Request a sample & Talk to us about your objective.