Bounded pilot
Targeted Hardening Cohort
Checked synthetic clinical worlds targeted to one measured weakness and packaged for a frozen customer-run or Synset-supported hardening experiment.
Discuss a hardening cohortProducts
From translation readiness to recurring assurance.
Evaluate fit, map what is blocking external review, harden one supported weakness, test the result on held-out real data, and preserve the case for future updates.
Evaluate fit
Evaluate fitDetermine whether Synset fits the model, data, and intended use.
Share high-level, non-PHI context about the model, its stage, the intended use, the weakness you are trying to address, and whether a held-out evaluation path exists.
Translation readiness
Define the shortest defensible path forward
Identify what is blocking external review, determine what can be addressed, and decide what evidence or intervention should come next.
01 / Available now
Translation Readiness Assessment
Buyer
Clinical AI teams moving a promising prototype toward external review, an enterprise pilot, or a bounded hardening decision.
What Synset packages
Identify what is preventing a clinical AI model from moving toward external review, determine which gaps can be addressed, and define the shortest defensible path forward.
Process
- 01Define use
- 02Map translation blockers
- 03Determine what can be addressed
- 04Prioritize hardening opportunities
- 05Plan proof
Differentiation
Stress testing, subgroup analysis, coverage analysis, calibration review, evidence-gap identification, and model diagnostics are methods inside the assessment, not separate product identities.
translation-blocker map
what can be addressed
hardening priorities
held-out proof plan
next-decision recommendation
Model hardening
Attempt one bounded intervention
Generate targeted clinical data for one supported weakness, apply an agreed intervention, and freeze the candidate before final evaluation.
02 / Bounded pilot
Model Hardening Pilot
Buyer
Teams with a supported model, a declared use, a reproducible adaptation path, and one measured weakness that may be correctable through targeted data or another bounded intervention.
What Synset packages
Address one supported, measured model weakness with targeted synthetic clinical data and test whether the change holds up on held-out real data.
Process
- 01Select one gap
- 02Build targeted cohort
- 03Apply intervention
- 04Freeze the candidate
- 05Compare on held-out real data
Differentiation
The intervention may be customer-run, Synset-supported, or performed by an approved implementation partner. The candidate is frozen before final evaluation.
Open to selected design partners. Closed-loop customer-model hardening is not yet a completed public proof or a universal autonomous retraining capability.
correctability decision
targeted hardening cohort
frozen intervention record
held-out comparison plan
remaining-risk decision
Regression
Preserve the failures that matter
Turn discovered weaknesses into fixed tests that can be rerun after future model and workflow changes.
03 / Recurring expansion
Regression Suite
Buyer
Clinical AI teams shipping model, prompt, retrieval, threshold, or workflow updates.
What Synset packages
A fixed, versioned set of checked scenarios and observed failures rerun after each relevant system change.
Process
- 01Freeze the case
- 02Update the model
- 03Rerun the suite
- 04Compare results
- 05Accept, hold, or investigate
Differentiation
Results are reported as within threshold, regression detected, review required, or outside predefined tolerance.
frozen baseline suite
change-impact report
regression deltas
failure clusters
versioned evidence record
Enterprise design partner
Held-Out Hardening Study
A frozen design-partner intervention evaluated on held-out real data, with prespecified success criteria, no-regression gates, and a technical evidence package.
Design a held-out studyAssurance and local validation
Maintain evidence as the system changes
Enterprise design partners can maintain scoped regression evidence and evaluate models where customer-controlled real data already lives.
04 / Enterprise design partner
Continuous Assurance
Buyer
Clinical AI vendors and enterprise teams maintaining evidence across recurring system changes.
What Synset packages
Recurring regression testing and evidence maintenance across model, prompt, retrieval, threshold, workflow, and population changes within a declared evaluation scope.
Differentiation
Continuous assurance preserves scoped evidence against declared changes and known failure surfaces. It does not mean every future failure will be detected.
05 / Enterprise design partner
Local Validation
Buyer
Health systems and model vendors that need authorized evaluation where customer-controlled models and real data already live.
What Synset packages
An institution-local path for authorized evaluations, customer-controlled held-out comparison, and protected aggregate reporting.
Differentiation
Raw patient data can remain inside the customer environment. Customer-ready local deployment is not yet a generally available product.
Early customers
Built for teams moving clinical AI into practice.
Clinical AI startups
Teams moving from a promising prototype toward an external pilot or evidence-backed product decision.
Translational laboratories and physician-led spinouts
Research teams that need a practical path from a measured model result to a bounded development plan.
Digital-health vendors
Product teams preparing models and workflows for enterprise evaluation and deployment.
Healthcare and life-sciences model teams
Builders working with sparse, delayed, or underrepresented clinical data and patient contexts.
Compounding platform
From immediate value to infrastructure.
Each translation engagement creates reusable methodology, persistent evidence, and customer-specific regression infrastructure.
Growing platform
Clinical Hardening Library
A governed library of blind-spot families, controlled scenario templates, clinical mechanism patterns, intervention strategies, and, when demonstrated, the conditions under which interventions succeed or fail.
Customer-specific findings remain segregated. Reusable methodology is retained only where contractually permitted.
Building
Evidence Record
Persistent traceability across model versions, identified gaps, intervention protocols, evaluation results, evidence limitations, and future updates.
The current product produces structured reports and artifacts; the persistent platform layer is still being built.
Preview the Evidence Record →Today / recurring expansion
Regression Assets
Fixed customer-specific tests that preserve discovered blind spots, important evidence conditions, previously identified regressions, corrected regressions where correction has been demonstrated, and future model-update checks.
Customer-specific regression cases are not reused across customers without authorization.
Specialist offerings
Specialized Model Assessments
Bounded documentation, coding, and risk-model assessments remain available for teams with a focused evaluation question. They support the translation workflow rather than define the company category.
Available now
Documentation Model Assessment
A bounded assessment of unsupported assertions, omitted critical facts, contradictions, medication or lab mismatches, temporal errors, and sensitivity to documentation shifts.
Assess a documentation modelAvailable now
Coding / CDI Model Assessment
A bounded assessment of missed codes, unsupported codes, upcoding risk, diagnosis hallucination, and inconsistency between notes and structured evidence.
Assess a coding modelAvailable now
Risk Model Assessment
A bounded assessment of calibration, false negatives, subgroup behavior, missingness, drift, and risk coverage under controlled clinical conditions.
Assess a risk modelSupporting packages
Clinical-world material for development and QA.
Realistic synthetic clinical worlds are clinically coherent records, trajectories, evidence conditions, and scenarios. They can also support QA, integration, and controlled edge-case work without becoming the primary product category.
Available now
Synthetic Scenario and QA Packages
Checked patient scenarios, records, timelines, notes, and controlled variants for development, QA, demonstrations, benchmarks, and bounded assessments.
Request a scenario or QA packageAvailable now
FHIR / EHR Sandbox Blueprint
Clinically coherent, longitudinal, scenario-controlled test populations for integration tests, staging, demonstrations, and workflow simulation without PHI.
Request sandbox blueprintEnterprise design partner
Rare-State Scenario Pack
Bounded scenario families for rare states, sparse evidence, unusual combinations, and clinically important edge conditions.
Discuss a rare-state packHow an engagement runs
Customer provided
1. Establish approved access
The customer provides a model interface or response export, declared clinical use, target or expected-output definition, available development information, and a held-out real evaluation path.
Synset-supported
2. Map the contract
Synset validates the input and output contract, records model and data versions, identifies candidate failure surfaces, and defines the evaluation scope with the customer.
Synset workflow and review
3. Generate and check scenarios
The workflow creates targeted synthetic clinical worlds, applies clinical, temporal, evidence, and release checks, and freezes the cohort and manifest for the study.
Agreed with the customer
4. Select the intervention
The intervention may be customer-run training, Synset-supported bounded training, calibration, or another scoped model change. No intervention is recommended when the weakness cannot be addressed responsibly.
Customer-controlled evaluation
5. Compare model versions
The original and updated models are compared on held-out real data using prespecified success, no-regression, subgroup, and calibration checks where applicable. The final holdout is not used to choose or tune the intervention.
Workflow output and review
6. Preserve the result
The engagement produces an evidence record, remaining-risk map, regression tests, and a recommendation to proceed, collect more real evidence, narrow the use, or stop.
Reusable software layer: versioned probe suites, scenario generation, artifact checks, manifests and hashes, response import, scoring and reporting, and regression cases.
Human or partner work: intended-use definition, clinical review, intervention selection, model-specific integration, regulatory interpretation, and release decisions.
Automation depends on model type and access. Current hardening pilots remain bounded, versioned engagements rather than universal autonomous retraining. Timing is scoped during intake.
Current model scope
- Scenario-based text systems can be assessed when responses can be imported or reached through an approved OpenAI-compatible endpoint. This can include clinical assistants, RAG workflows, documentation models, and coding/CDI models.
- Structured prediction models, including risk models, are scoped case by case and require reproducible inference, a defined target, and suitable evaluation data. Longitudinal or survival models require separate review of time horizon, target, censoring, and validation design.
- API-only and closed commercial systems may be assessed when responses can be captured through an approved path. They cannot be hardened unless a reproducible training or adaptation route is available.
- Hardening pilots require customer-approved access, a frozen intervention, and customer-controlled held-out real data. Imaging, waveform, voice, and unsupported modalities remain partner-driven.
Roadmap and partner-driven directions
These are not current validated products unless a specific evidence artifact marks a use case as validated.
Imaging descriptors
Roadmap or partner-driven unless a specific evidence artifact marks a use case as validated.
Waveform descriptors
Roadmap or partner-driven support for workflow stress testing, not a validated current modality.
Clinical speech and dialogue simulation
Roadmap or partner-driven unless separately released with evidence and claim boundaries.
Life-sciences / RWE exploration
Exploration area for partner studies, not a public claim of broad real-world evidence validity.
Trial-feasibility exploration
Not synthetic control arms as a current product, and not a replacement for real-world validation.
Start with a translation assessment.
Bring one model, one intended use, and one failure question. Synset will map the evidence and determine whether a bounded intervention is justified.
Available now
Translation Readiness Assessments.
Selected design partners
Model Hardening Pilots and customer-controlled local evaluation paths.