Development data
Build or improve task-specific behavior.
Labs
Healthcare evaluations
for teams building AI.
SingleAxis works with teams training healthcare and life-science AI to scope expert-grounded evaluation datasets, development data, rubrics, and workflow test cases.
Request brief
01
SystemWhat is being trained or developed
02
TaskWhat work it must perform
03
EvidenceWhat data or evaluation is needed
04
ExpertiseWho must create or review it
05
ConstraintsPrivacy, modality, and delivery needs
Model development
Evaluation should begin before release.
The cases used to improve a system and the cases used to judge it serve different purposes. Keeping that distinction clear makes later testing more useful.
Held-out evaluation
Measure behavior on cases kept outside training.
Pre-release testing
Test the model and system in the intended context.
Regression set
Retest known risks as the system changes.
What can be requested
Scope the evidence the system needs.
Requests are reviewed against the task, required expertise, data constraints, and intended use. Availability is confirmed only after that review.
Capability not yet publicly substantiated: public customer case studies and released Labs datasets are not currently available.Training and development data
Examples and supporting context for domain-specific model development and post-training work.
Scoped per requestHeld-out evaluations
Cases kept separate from training for pre-release, robustness, and regression testing.
Scoped per requestExpert rubrics and grading
Review criteria and adjudication plans grounded in clinical or scientific judgment.
Scoped per requestWorkflow test cases
Tasks that include context, decisions, tools, actions, and human oversight requirements.
Scoped per requestAdversarial and long-tail cases
Difficult, ambiguous, and high-consequence situations selected for targeted evaluation.
Scoped per requestBiomedical and scientific tasks
Requests involving life-science research work can be reviewed for relevant expertise and feasibility.
Scoped per requestWho Labs is for
For teams training healthcare and life-science AI.
Model developers
Teams training foundation, specialist, or fine-tuned models for healthcare use.
Healthcare AI builders
Product teams developing systems that operate inside clinical or operational workflows.
Pharma and biotech
Teams building AI for biomedical research, drug development, and scientific work.
Research groups
Clinical and academic teams studying safer deployment, monitoring, and evaluation methods.
Dataset development
Build from the task backward.
Useful evaluation data begins with a defined decision or workflow, then specifies the evidence needed to assess it.
Define the task
Specify the work the model or system is expected to perform.
Map the context
Identify inputs, tools, users, constraints, and operating conditions.
Design the cases
Select representative, difficult, and high-consequence situations.
Build the rubric
Define expected behavior, acceptable variation, and failure criteria.
Review and adjudicate
Set the expertise and review process required for defensible labels.
Version and hold out
Separate evaluation material and preserve it for later regression testing.
Evaluation boundary
Keep evaluation separate from training.
Held-out cases, versioned rubrics, and documented review make it possible to distinguish model improvement from familiarity with the test material.
Development set
Model / system
Held-out evaluation
Labs inquiry
Tell us what you are building.
Describe the model or system, the task it must perform, and the data or evaluation evidence you need. We will review fit and feasibility before proposing a scope.
- Healthcare and life-science focus
- Training and evaluation requests separated
- Expertise and data constraints reviewed first