Don't assume. Verify.
Awareness Verify continuously adversarially tests AI agents and models, compares behavior with known-good releases, and can stop a serious regression before it reaches users.
Break your AI before you ship it.
Initial cohort: up to 10 organizations. One authorized pre-production AI system per pilot.
AI behavior becomes a testable deployment condition.
Ordinary software tests can pass while an AI system changes how it uses tools, handles approval, follows poisoned context, responds under pressure, or behaves after a model or prompt update.
Adversarial testing
Replay security, behavioral, tool, MCP, authorization, and reliability scenarios against the system that is actually being shipped.
Regression evidence
Compare a candidate with an approved baseline or previous run and distinguish introduced, persistent, and resolved findings.
Release gating
Turn reviewed policy into a machine exit code so a serious new failure can stop a deployment while preserving the evidence needed to investigate it.
One real staging system. One useful assurance loop.
The pilot is deliberately narrow. The goal is to prove the workflow on a system your team already owns and understands, not to spend a month in architecture meetings.
Connect safely
Choose one authorized pre-production agent, RAG system, MCP-connected workflow, copilot, or model-backed application. Start without production side effects.
Run the first assessment
Capture JSON and human-readable evidence, review the scenario scope, and identify false positives, blind spots, and the boundaries that matter most.
Establish known-good behavior
Approve or retain a reviewed baseline, then make one controlled change that should violate a tested boundary.
Prove the gate
Verify should detect the regression, block under the reviewed policy, preserve evidence, and return to PASS after remediation.
Founder-led integration, not a self-serve handoff.
days
Private-preview access for one staging system and one primary release workflow.
software fee
No software license fee for the initial pilot. Post-pilot Team/Enterprise needs are scoped separately.
onboarding
Direct help selecting the first test surface, configuring the assessment, reviewing the report, and proving the controlled regression cycle.
Teams shipping AI systems that change often enough for regression to matter.
Strong first pilots
AI product teams, internal AI platforms, agents with tools, RAG applications, MCP-connected systems, regulated or security-sensitive AI workflows, and teams already doing manual red-team or evaluation work.
Not the first place to start
Production-only systems with no safe test target, systems you are not authorized to adversarially test, or engagements that require real destructive side effects just to prove the workflow.
PASS means the reviewed tests passed. Nothing more.
Awareness Verify is built to make evidence stronger and uncertainty more visible, not to turn a limited test into a universal safety claim.
Observed behavior first
Prefer wrapped tools, MCP calls, authorization evidence, and traces over asking the model to describe what it did.
Semantic uncertainty is explicit
Optional semantic evaluators can repeat judgments, measure disagreement, route uncertainty to review, and be empirically calibrated on labeled sets.
Capability can escalate controls
Versioned Frontier profiles can require stronger safeguards and evaluation integrity as measured capability and uncertainty approach reviewed thresholds.
Useful locally. Stronger when teams need coordination.
Community direction
Local and CI testing, built-in and custom scenarios, adapters, regression evidence, semantic evaluation, local history, reports, and synthetic demos. The public licensing decision is still being finalized, so we are not calling the current private preview open source.
Team / Enterprise direction
Shared history, Team Assurance web review, RBAC/OIDC, audit evidence, private runners, organization policy, enterprise reporting, maintained intelligence packs, deployment support, and higher-assurance private operating patterns.
Put one real AI release through the loop.
Tell us what the system does, whether a staging target exists, what stack it uses, and the behavior you most want to stop from regressing.