Prove Your AI Worksto Experts, Auditors and Regulators.
Our domain experts build test sets from your own cases, calibrate automated judges to your reviewers, red-team for injection and data leakage, and assemble the validation package your quality, security or authorization team needs.
- VLValidation lead
- DEDomain experts
- EEEvaluation engineer
- STSecurity tester
- An expert-labeled test set
- Calibrated judges with agreement scores
- Red-team findings and fixes
- A validation report with traceability
Regulators Now Ask How You Know the AI Is Right
A vendor demo is not evidence. Quality, security and authorization teams need tests, traceability and results that stay current.
in the FDA’s risk-based credibility framework for AI that supports regulatory decisions, from context of use to adequacy.
How we fix itOur validation package follows the same structure: question, context of use, risk, plan, results and adequacy.
Source: FDA draft guidance, January 2025of enterprises will demote or decommission production agents after incidents expose governance gaps.
How we fix itRed-teaming and regression suites find the gaps before an incident does.
Source: Gartner, May 2026of agentic AI projects will be canceled by 2027, with inadequate risk controls among the top causes.
How we fix itEvidence is mapped to the controls you answer to and regenerated on every change.
Source: Gartner, June 2025Evidence, Not Assurances
Regulated teams cannot ship on a vendor demo. We produce the evidence that an agent does what it should, fails safely, and keeps doing so after every change.
Expert-Labeled Test Sets
Pharmacovigilance, regulatory, quality and records experts label real cases into gold-standard answers.
Judge Calibration
Automated graders tuned until they agree with your reviewers, with agreement reported for each criterion.
Red-Teaming
Prompt injection, data leakage, jailbreaks and out-of-scope requests tested by our security team.
Bias and Consistency Review
Results compared across document types, sources and groups, so gaps are found before rollout.
Validation Documentation
Intended use, risk assessment, test protocols, results and traceability, written in your quality system’s format.
Regression Suites
The same cases rerun on every model, prompt or data change, with the results kept for audit.
Six Weeks to an Audit-Ready Package
- Week 11
Plan
Intended use, risks and acceptance criteria agreed with your quality or security lead.
- Weeks 1–42
Label
Domain experts label real cases into a gold-standard test set, with a second reviewer on disagreements.
- Weeks 3–53
Test
Judges calibrated, the agent scored, and red-team findings fixed and re-tested.
- Weeks 5–64
Package
Protocols, results, traceability and the summary report delivered for sign-off.
Estimate the Review Time You Save
Calibrated judges take over routine grading, so your experts review only what needs them.
- Baseline measured in week one, from your systems
- Hours, cycle time, cost and quality reported monthly
- Every assumption written down and signed off
What You Get
A package your reviewers can follow from requirement to result, and a test suite that keeps it current.
In the validation package
- An intended use statement and risk assessment
- An expert-labeled test set drawn from your own cases
- Test protocols with acceptance criteria
- Calibrated judges with agreement scores for each criterion
- Red-team findings, fixes and re-test results
- A traceability matrix from requirements to test results
- A regression suite that reruns on every change
Validated Against the Standards You Answer To
Evidence is mapped to the controls you name and written in the format your reviewers expect.
Life Sciences
- 21 CFR Part 11
- EU GMP Annex 11
- GAMP 5, Second Edition
- FDA Computer Software Assurance
Federal
- NIST AI Risk Management Framework
- OMB AI use requirements
- FedRAMP and agency ATO evidence
- FISMA controls
Enterprise
- ISO/IEC 42001
- EU AI Act
- SOC 2
- HIPAA
Why Auditors Accept the Evidence
Evidence is only as good as the people who labeled it and the process that keeps it current.
Labeled by Practitioners
The people who label the cases have done the work, not just read about it.
Second Reviewer on Disagreements
Contested labels go to a second expert, and the resolution is recorded.
Traceable to Requirements
Every test links back to a requirement, a risk and a result.
Kept Current
Evidence is regenerated on every change, not filed once and forgotten.
Test Data Stays Inside
Test sets and results stay in your environment and never train a model.
In Your Format
Documents follow your quality system’s templates, so reviewers know where to look.
Related Modules
The platform modules these solutions are built on.
Other Services
Frequently Asked Questions
Common questions about ThinkTrends Evaluation & Validation.
How do you validate an AI agent for GxP use?
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Define the intended use and risk, build a test set of real cases labeled by domain experts, set acceptance criteria, score the agent with calibrated judges and human review, test failure modes, and document the results with traceability to requirements. ThinkTrends follows a risk-based approach consistent with GAMP 5 and FDA Computer Software Assurance.
What does the FDA expect when AI supports a regulatory decision?
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The FDA’s January 2025 draft guidance sets a seven-step, risk-based credibility framework: define the question of interest, define the context of use, assess model risk, plan the credibility assessment, carry it out, document results and deviations, and decide whether the model is adequate. ThinkTrends Evaluation and Validation produces evidence in that structure.
What is an expert-labeled test set?
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A set of real cases with correct answers written by people who do the work, such as safety physicians or regulatory specialists. It is the standard an agent is scored against before release and after every change.
Can you support a federal ATO for an AI system?
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Yes. We produce AI-specific evidence for your authorization package, including intended use, risk assessment, test results, red-team findings and monitoring plans, mapped to the NIST AI Risk Management Framework and your agency’s controls.
How often do agents need to be re-validated?
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Whenever the model, prompt, tools or source data change, and on a schedule you set. The regression suite reruns automatically and the evidence is regenerated.
What happened to ThinkTrends Data Labeling services?
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Data labeling is now part of Evaluation and Validation. Our domain experts still label data, and now focus on the test sets and training examples that prove and improve agent quality in regulated work.