Clinical data platforms
Ingestion, quality, review, generative analytics, and statistical programming on one AI-native control plane. Built for clinical research operators replacing the spreadsheet-plus-vendor-portal stack.
Sectors · Healthcare
Bench to bedside, on a single surface. Clinical research and digital health move at the speed of their slowest pipeline. We rewire underneath, so the model, the reviewer, and the patient all read from the same source of truth.
Currently in the field
Selected · not exhaustive
Operating truths
Every record carries who captured it, what model touched it, who reviewed and when. The source travels with the data, end to end. Across every module on the platform.
Mappings, edit checks, reviewer policies, prompt templates — all first-class versioned artifacts. Changes are diffable. Decisions are replayable. Nothing ships unsigned.
The agent generates. The verifier grounds it. The reviewer takes the disposition. No autonomous decisions on patient data. Not now. Not ever.
Ingest, transform, review, analyze, report — all on one surface. No warehouse hop. No second source of truth. No vendor portal sprawl.
What we build
Ingestion, quality, review, generative analytics, and statistical programming on one AI-native control plane. Built for clinical research operators replacing the spreadsheet-plus-vendor-portal stack.
Intake automation, eligibility verification, prior authorization, and patient-flow orchestration. AI-assisted provider workflows where every minute of friction has a downstream cost on care.
Recursive grounding. AI-assisted authoring. The agent proposes, the engine runs, the verifier grounds. Every prompt, every result, every decision captured with its source.
Engagements
Six modules on a unified platform.
A unified, AI-native control plane across the trial lifecycle. Authoring, review, and analytics with humans in the loop at every step that matters.
12 wk to first deployment · 36 wk to Phase 1 live
Digital phenotyping infrastructure.
Hardened mobile data collection, high-speed ingestion, and an Auto-ML baseline. Production-grade infrastructure for behavioral signals at scale.
13 wk to MVP · 4 milestones · ready to scale
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