Test AI the way reality will.
Suite of products to predict, attribute, and diagnose AI risk.
Continuous assurance, not one-time validation.
Prove agents before they ship.
The testing and validation simulation platform that uses real-world personas and scenarios. Before an AI system reaches a real user, AI Range puts it through the paces: adversarial personas, realistic scenarios, and multi-turn conversations built to surface the failures a static benchmark never will.
Know what to test.
A 9-section intake maps the system's real interaction surface: access, autonomy, action, accountability. The Alignment Readiness Score (0 to 100) shows where risk concentrates before a single test runs. Context governs test selection, so effort lands where your topology says it should, not where a generic suite defaults.
Maximize test spend coverage while detecting new vulnerabilities.
748 adversarial prompts and 37 MITRE ATLAS techniques, selected by context instead of run blindly. Ten concurrent executors concentrate spend on the highest-risk paths, and multi-turn adversarial pressure surfaces failure modes static benchmarks never reach. Coverage you can show. Findings you could not have bought elsewhere.
Watch a run, end to end.
The complete verification workflow runs on its own. Click any stage to replay just that stage.
From system intake to examiner-ready evidence. One continuous verification workflow.
Keep proving them in production.
From choosing a model to watching it in production, Peregrine is the mathematical and predictive layer behind the signals that matter. Guardrails decay over time. We call this guardrail drift: the slow erosion of AI alignment as real-world usage pulls behavior away from intended constraints.
Watch the math work.
Know when to intervene.
Peregrine is designed to predict failure 2 to 6 turns before a guardrail breaches. Not whether a breach happened: how fast one is approaching, and how long you have to act. Intervention becomes a scheduled operation instead of an incident response.
Attribute every risk to its source.
Built to attribute risk across models, agents, and components, so a drifting behavior traces back to the piece of the system that caused it. You fix the component, not the symptom, and the fix carries into the next Evidence Pack as proof.
Features and what they buy you.
Failure predicted 2 to 6 turns before it breaches. Intervention becomes scheduled work, not incident response.
How much a given model amplifies your specific risk, evaluated before it is even chosen.
Drift measured in real time as it happens, not discovered after the fact.
Cumulative risk scoring: model drift quantified release over release.
Failure traced across models, agents, and components. Fix the cause, not the symptom.
Continuous evidence for regulators that assurance did not stop at deployment.
Know what you can prove.
Every test AI Range runs and every signal Peregrine tracks lands in one place: a signed, tamper-evident record of how the system actually behaves. Tap or hover a section of the record to jump to why it matters.
What the system was asked. Every scenario executed in this run, recorded verbatim: adversarial prompts, MITRE ATLAS techniques, MICE scenarios, permission probes, and multi-turn drift sequences, with selection rationale.
Signed and hash-chained. The SHA-256 chain of custody proves the record has not been altered since the run. Examiners start with provenance; the Pack leads with it.
Meets examiners where they already work. NIST AI RMF, OWASP LLM Top 10, MITRE ATLAS, SR 26-2, EU AI Act: one artifact, every framework.
Behavior, not intent. The record shows what the system actually did under adversarial pressure: every tool call, every handoff, replayable.
What passed and what failed. Five dimensions scored per run. Failures are documented with the same rigor as passes, because a credible record needs both.
Confidently deploy AI agents.
Optica Labs helps organizations deploy AI confidently by identifying risk, stress-testing systems, and enforcing clear, accountable governance.