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Scalefresh AI

Scalefresh AI

Evaluation infrastructure that makes healthcare AI provable

Evaluation infrastructure that makes healthcare AI provable

We build the full evaluation stack for healthcare AI: instrumentation, golden datasets, domain-specific rubrics, failure taxonomies, calibrated evaluators, CI/CD eval gating, and model card production. Continuous measurement that tells you what is working, what is not, and where the fix is.

Talk to us about your AI systems

Talk to us about your AI systems

See the methodology

See the methodology

Evaluation Is Infrastructure,
Not A Testing Phase

Most organizations treat AI evaluation as something that happens once before deployment: run some test cases, check the accuracy number, ship it. In healthcare, that approach leaves you unable to answer basic questions with any precision.


How often is it wrong?

What kind of wrong?

Which patients or item types are most affected?

Is it getting better or silently degrading?


Our evaluation infrastructure answers those questions on an ongoing basis. It is a permanent layer of your AI architecture that measures, diagnoses, and improves your systems continuously.

What We Build, Component By Component

Each component produces independent value and is engineered to work with the others. Organizations typically begin wherever their infrastructure stops on our five-tier maturity model and build progressively from there.

  1. Observability layer

Trace-level instrumentation across every AI system


Every LLM call, retrieval event, tool invocation, and agent decision captured as a structured trace with queryable metadata: prompt text, completion text, model version, token counts, latency at each span, and identifiers tying every output to a specific patient, session, and experiment. The trace hierarchy mirrors your actual system architecture so multi-step pipelines read as single traceable events. This is the observation layer everything else depends on.

  1. Ground truth

Golden datasets built with your clinical SMEs


A labeled dataset of known-correct outputs for your specific workflows, built directly with your clinicians rather than generated synthetically. Real clinical scenarios covering the distribution of cases your AI encounters in production, with edge cases and high-risk item types weighted deliberately. The golden dataset is the ground truth your entire evaluation system scores against, and its quality determines the ceiling of everything downstream.

  1. Clinical logic

Domain-specific rubrics for every item type under evaluation


Asking "is this OASIS item coded correctly" requires more than a yes or no. The rubric needs to capture the actual CMS coding logic for that specific item, spelling out what clinical evidence supports each code value, what documentation would justify it, and what the most common misapplication patterns look like in practice. We build rubrics at this level of specificity because generic scoring misses the clinical nuance that determines whether an output would hold up under audit.

  1. Diagnostic engine

A four-bucket failure taxonomy that routes each error to the right fix


Knowing an output is wrong is only the beginning. The taxonomy categorizes each error so the fix goes to the right layer of the system.


Fabrication: model generated evidence that does not exist in the source. Fix is in the retrieval layer.


Documentation gap: evidence exists clinically but was never in the data the model received. Fix is in the data pipeline.


Wrong abstention: model had sufficient evidence but refused to answer. Fix is in prompt calibration.


Generation coding error: model attempted the item and applied wrong clinical reasoning. Fix is in the reasoning instructions.


Without the taxonomy, every error looks like a prompt problem. With it, only one of the four buckets actually is.

  1. Scoring layer

Three evaluator types running in concert


Rule-based scorers cover deterministic checks like required fields, valid code sets, and assessment window dates. LLM-judge scorers apply rubrics to ambiguous cases, evaluating whether the clinical reasoning matches the logic a given item type requires. Human-in-the-loop annotation validates both and continuously feeds new labeled examples back into the golden dataset. LLM-judge scorers are calibrated on a scheduled cadence against human judgment until alignment reaches the threshold your clinical team requires.

  1. Experiment infrastructure

Structured experiments with full provenance


Every evaluation runs as a structured experiment with defined scope: which items, which patient cohort, which prompt version. Traced execution and scored results stored with complete provenance, so you can compare prompt version A against version B across the same golden dataset and see exactly where performance shifted, by how much, and in which direction.

  1. Deployment integration

CI/CD eval gating


Evaluation wired into the deployment pipeline so every change to prompts, retrieval, or model version triggers a traced evaluation run against the golden dataset. Results are scored automatically, and changes that drop performance below defined thresholds do not ship. Your release process and your evaluation process become one and the same.

  1. Governance artifacts

Model cards and claim-level evidence linkage


Every AI system in production receives a CHAI-aligned model card: a living artifact documenting what the system does, how it was evaluated, what its limitations are, and how it performs across subgroups. This is what procurement teams and regulators are beginning to require.


Evidence linkage traces every claim in a generated output back to the specific source documentation that supports it, showing which evidence supports the output, how strong the support is, and whether contradictory evidence exists. This is the layer that makes AI outputs auditable.

  1. Self-improvement Loop

HITL override analysis and continuous learning loops


When a clinician overrides an AI recommendation, that override is captured, categorized, and fed back into the golden dataset. Over time, override patterns reveal systematic gaps that evaluation metrics alone would miss. Failure taxonomy analysis shows which error categories are declining and which persist. Experiment results surface which changes produced genuine improvement. The evaluation system measures itself measuring the AI, and both layers improve in concert.

  1. Observability layer

Trace-level instrumentation across every AI system


Every LLM call, retrieval event, tool invocation, and agent decision captured as a structured trace with queryable metadata: prompt text, completion text, model version, token counts, latency at each span, and identifiers tying every output to a specific patient, session, and experiment. The trace hierarchy mirrors your actual system architecture so multi-step pipelines read as single traceable events. This is the observation layer everything else depends on.

  1. Ground truth

Golden datasets built with your clinical SMEs


A labeled dataset of known-correct outputs for your specific workflows, built directly with your clinicians rather than generated synthetically. Real clinical scenarios covering the distribution of cases your AI encounters in production, with edge cases and high-risk item types weighted deliberately. The golden dataset is the ground truth your entire evaluation system scores against, and its quality determines the ceiling of everything downstream.

  1. Clinical logic

Domain-specific rubrics for every item type under evaluation


Asking "is this OASIS item coded correctly" requires more than a yes or no. The rubric needs to capture the actual CMS coding logic for that specific item, spelling out what clinical evidence supports each code value, what documentation would justify it, and what the most common misapplication patterns look like in practice. We build rubrics at this level of specificity because generic scoring misses the clinical nuance that determines whether an output would hold up under audit.

  1. Diagnostic engine

A four-bucket failure taxonomy that routes each error to the right fix


Knowing an output is wrong is only the beginning. The taxonomy categorizes each error so the fix goes to the right layer of the system.


Fabrication: model generated evidence that does not exist in the source. Fix is in the retrieval layer.


Documentation gap: evidence exists clinically but was never in the data the model received. Fix is in the data pipeline.


Wrong abstention: model had sufficient evidence but refused to answer. Fix is in prompt calibration.


Generation coding error: model attempted the item and applied wrong clinical reasoning. Fix is in the reasoning instructions.


Without the taxonomy, every error looks like a prompt problem. With it, only one of the four buckets actually is.

  1. Scoring layer

Three evaluator types running in concert


Rule-based scorers cover deterministic checks like required fields, valid code sets, and assessment window dates. LLM-judge scorers apply rubrics to ambiguous cases, evaluating whether the clinical reasoning matches the logic a given item type requires. Human-in-the-loop annotation validates both and continuously feeds new labeled examples back into the golden dataset. LLM-judge scorers are calibrated on a scheduled cadence against human judgment until alignment reaches the threshold your clinical team requires.

  1. Experiment infrastructure

Structured experiments with full provenance


Every evaluation runs as a structured experiment with defined scope: which items, which patient cohort, which prompt version. Traced execution and scored results stored with complete provenance, so you can compare prompt version A against version B across the same golden dataset and see exactly where performance shifted, by how much, and in which direction.

  1. Deployment integration

CI/CD eval gating


Evaluation wired into the deployment pipeline so every change to prompts, retrieval, or model version triggers a traced evaluation run against the golden dataset. Results are scored automatically, and changes that drop performance below defined thresholds do not ship. Your release process and your evaluation process become one and the same.

  1. Governance artifacts

Model cards and claim-level evidence linkage


Every AI system in production receives a CHAI-aligned model card: a living artifact documenting what the system does, how it was evaluated, what its limitations are, and how it performs across subgroups. This is what procurement teams and regulators are beginning to require.


Evidence linkage traces every claim in a generated output back to the specific source documentation that supports it, showing which evidence supports the output, how strong the support is, and whether contradictory evidence exists. This is the layer that makes AI outputs auditable.

  1. Self-improvement Loop

HITL override analysis and continuous learning loops


When a clinician overrides an AI recommendation, that override is captured, categorized, and fed back into the golden dataset. Over time, override patterns reveal systematic gaps that evaluation metrics alone would miss. Failure taxonomy analysis shows which error categories are declining and which persist. Experiment results surface which changes produced genuine improvement. The evaluation system measures itself measuring the AI, and both layers improve in concert.

What You Actually Receive

What You Actually Receive

1

Instrumented AI systems

Trace-level observability with structured persistence and queryable metadata across every system under evaluation.

1

Instrumented AI systems

Trace-level observability with structured persistence and queryable metadata across every system under evaluation.

2

Calibrated Evaluation System

Golden dataset, rubrics, failure taxonomy, and evaluators running against labeled ground truth, measuring performance at the item, claim, and patient level.

2

Calibrated Evaluation System

Golden dataset, rubrics, failure taxonomy, and evaluators running against labeled ground truth, measuring performance at the item, claim, and patient level.

3

Eval-Gated Deployment

Evaluation wired into CI/CD so changes are scored before they ship and regressions are caught automatically.

3

Eval-Gated Deployment

Evaluation wired into CI/CD so changes are scored before they ship and regressions are caught automatically.

4

Model Cards

CHAI-aligned documentation for every production system, produced as a deployment artifact that stays current.

4

Model Cards

CHAI-aligned documentation for every production system, produced as a deployment artifact that stays current.

5

Evidence Linkage

Claim-level traceability connecting every AI output to the source evidence that supports it.

5

Evidence Linkage

Claim-level traceability connecting every AI output to the source evidence that supports it.

6

Monitoring Dashboards

Fabrication rate, accuracy by item type, latency by span, evaluator agreement, and override patterns in real time.

6

Monitoring Dashboards

Fabrication rate, accuracy by item type, latency by span, evaluator agreement, and override patterns in real time.

When This Service Is The Right Fit

When This Service Is The Right Fit

When This Service Is The Right Fit

We have AI in production and no structured way to evaluate it.

The most common starting point. We begin with instrumentation and build toward full structured evaluation.



We have AI in production and no structured way to evaluate it.

The most common starting point. We begin with instrumentation and build toward full structured evaluation.



A buyer, regulator, or board is asking us to prove our AI works.

We build the evaluation infrastructure and model card documentation that satisfies that with engineering rigor.



We want evaluation built in from day one.

The ideal engagement. Architecting evaluation alongside the system is significantly less expensive than retrofitting it later.



We have basic logging but no golden dataset, rubrics, or failure taxonomy.

Tier 2 on our maturity model. We build you to Tier 3 and beyond.



Our evaluation exists but is not wired into deployment.

Tier 3. We take you through CI/CD integration, model cards, and continuous governance.



We are expanding AI into new workflows and need the evaluation methodology to scale with it.

We extend your existing evaluation infrastructure to cover new clinical or administrative workflows, building the rubrics, golden dataset additions, and failure taxonomy extensions each new workflow requires.



Common Questions

Common
Questions

How is this different from RPA or workflow automation?

What percentage of cases do agents handle autonomously?

Do agents replace clinical or administrative staff?

How long before agents are handling real workload?

Can we start with one workflow and expand?