AI for healthcare research and product teams.
Less-restricted inference for biomedical work, with the audit trails your governance reviews require.
Healthcare AI teams need to query and generate against medical literature without provider-side refusals interfering with legitimate research — and they need decision logs when their products ship into privacy, safety, and clinical reviews.
Why teams in healthcare research & tech hit a wall.
Refusals block legitimate medical research
Querying about drug interactions, dosing studies, contraindications, or rare-condition symptoms gets refused by general-purpose APIs. Researchers and product teams can't actually do the work.
Training data for medical NLP is hard to source
Building clinical NER, triage classifiers, or symptom-checkers needs realistic medical-language data. Off-the-shelf APIs refuse to generate the variety required for robust models.
Product reviews require an audit trail
Shipping a healthcare AI feature means internal privacy, safety, and clinical reviews. Generic completion APIs leave you without the decision logs those reviews want to see.
Built for healthcare research & tech workloads.
Less-restricted inference for biomedical work
Run literature queries, summarize papers, generate hypotheses, draft research notes. Your policy decides what's in scope — not provider-side refusal heuristics tuned for a consumer chatbot.
Synthetic data for medical NLP
Generate training and eval examples for clinical NER, intent classification, and triage models — synthetic by design, no real patient data involved.
Audit trails for product governance reviews
Every decision logged with policy ID, reason code, and scoped key. Stream into the review tooling your privacy, safety, and clinical reviewers already use.
Scenarios from the field.
Biomedical literature research
Query and summarize PubMed-scale corpora without refusal noise on legitimate clinical questions. Stay focused on the research, not the workarounds.
Synthetic clinical-language training data
Generate labeled symptom descriptions, fictional case studies, or triage scenarios for model fine-tuning. Useful when real datasets are restricted or unavailable.
Product safety eval sets
Generate adversarial test cases for your shipping medical AI feature. Track every example with reason codes for your internal safety review.
Designed for the frameworks your auditors care about.
If your team handles PHI, talk to us before deployment — we'll work through your specific requirements together rather than ship a checkbox.
- Decision metadata per callPolicy ID, reason code, scoped key — on every request.
- Per-project key scopingIsolate keys per study, app, or research program.
- Zero data retentionDefault; prompts and outputs are not used for training.
- Audit-ready loggingStream events to S3, Splunk, Datadog, Elastic, or Azure Monitor.
- Designed for governance reviewsLogs and policy versioning support internal product reviews.
- SOC 2 (in progress)Enterprise audits underway.
Ready to bring governance to your healthcare research & tech stack?
Talk to an engineer about your deployment, or grab an API key and start building today.