The systems reshaping government need independent evidence.
Agencies are adopting AI faster than they can evaluate it — and much of it falls outside traditional model risk guidance. SR 26-2 governs traditional models but deliberately excludes generative and agentic systems, leaving agencies to govern the highest-profile AI with the least established playbook.
ALT brings established model-evaluation discipline to that gap. We provide independent testing, evaluation, and governance for AI and machine-learning systems — from predictive models to LLMs and agentic tools — aligned with recognized AI risk frameworks (NIST AI RMF), so agencies can adopt AI on evidence rather than vendor assurance.
What AI assurance delivers
Assurance across AI and ML systems
Model & system evaluation
Test accuracy, robustness, and fitness for use through benchmarking, holdout testing, and stress cases.
Bias & fairness assessment
Measure disparate impact and fairness across groups using defined metrics and subgroup testing.
LLM & generative evaluation
Test generative systems for hallucination, prompt injection, and content provenance through red-teaming.
Agentic system testing
Test autonomous, tool-using systems for boundaries, authorization, failure modes, and safe operation.
Post-deployment monitoring
Design behavior, drift, and safeguard monitoring across functionality, security, and human oversight.
AI governance & documentation
Build governance, model cards, and controls mapped to recognized AI risk frameworks.
How an AI system earns trust
Scope the system & its risk
Clarify the system's purpose, intended use, impact, and the evaluation the decision requires.
Define evaluation criteria
Set performance, bias, robustness, and safety criteria and the tests that will measure them.
Test, benchmark & red-team
Run structured testing, benchmarking, and adversarial evaluation to see how the system behaves.
Report, govern & monitor
Deliver findings, governance guidance, and monitoring so assurance continues after deployment.
Questions we help AI adopters answer
- Does this system actually perform the way the vendor claims?
- How do we evaluate a generative or agentic system that falls outside traditional model guidance?
- Is the system biased, and would we detect unsafe or unintended behavior?
- Is our AI governance and documentation strong enough to withstand oversight?
Connected ALT Solutions services
Adopting AI you need to trust — and defend?
ALT provides independent evaluation, testing, and governance for the AI and ML systems behind agency decisions.