Scalefresh provides AI evaluation and engineering services to health systems and health tech companies. Every system we build ships with the measurement infrastructure to prove it works.
The problem
The Evaluation Gap
Healthcare AI fails more often from missing evaluation infrastructure than from inadequate models.
Most organizations deploying AI in healthcare are doing so without
A golden dataset built with clinical subject matter experts to measure accuracy against
A failure taxonomy that distinguishes fabrication from documentation gaps from reasoning errors
valuation wired into deployment so regressions are caught before they reach patients
We close that gap through three core services, with evaluation running through all of them.
What We Build
Our Services
Evaluation runs through every service we deliver. The data layer feeds it, the agentic workflows are monitored by it, and the evaluation system itself is what makes your AI provable.
How it Connects
How These Services Work Together
The conventional approach to healthcare AI is sequential: build the data layer, deploy the workflows, then evaluate. That sequence puts evaluation at the end, where it becomes a validation exercise rather than a diagnostic tool.
We structure engagements differently. Evaluation infrastructure goes in first, because the instrumentation and trace architecture decisions made at the start determine whether rigorous evaluation is even possible later. Data infrastructure and agentic workflows are built with evaluation wired in from day one, so that failures can be traced to their root cause across every layer of the system from the moment the first output is generated.
Phase 2:
Structured evaluation
Golden datasets, rubrics, failure taxonomy, evaluators, and experiments running against labeled ground truth. You know what is working and what is not.
Phase 3:
Continuous governance
Eval pipelines gating deployment. Model cards. Evidence linkage. HITL override analysis. Self-improvement loops. Audit-ready at every moment.
Data infrastructure and agentic workflows are built in parallel with whichever phase your organization is in, with evaluation shaping every architectural decision along the way.
How We Can Help
Every engagement starts with a diagnostic. We map your current evaluation infrastructure against our five-tier maturity model and scope the work based on where your infrastructure stops and where the highest-impact gaps are.
01
Starting at Tier 1 or 2
Weeks 1-4: Instrumentation, trace architecture, and persistence across your AI systems. Baseline observability established.
Weeks 5-12: Golden dataset construction with your clinical SMEs. Eval metrics, rubrics, and failure taxonomy defined. Evaluators built and calibrated. First structured experiments run against labeled ground truth.
Weeks 13-16+: CI/CD eval gating, model card production, and governance loops wired into your deployment pipeline.
02
03
Starting at Tier 4
Evaluation is in your pipeline but governance infrastructure is incomplete. We focus on model card production, evidence linkage, HITL override analysis, and self-improvement loops that take you to Tier 5.
04
Where organizations typically begin
The most common entry points for an engagement with Scalefresh, in order of frequency:
Technical Environments
We engineer within the systems and standards your organization already operates on, rather than requiring migration to new platforms.