Connect an organization's AI system, run a battery of evaluation tests on our infrastructure, and produce an examiner-ready assurance report. Grounded in the OWASP LLM Top 10 (2025), RAGAS quality metrics, and model-risk regulation.
Assessments
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Avg assurance
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across systems
Open findings
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0 critical
Dimensions
11
4 pillars
Recent assessments
Coverage
🛡️ OWASP LLM Top 10 2025📐 RAGAS quality metrics🧭 NIST AI RMF🏛️ SR 26-2 · NAIC · NYDFS🇪🇺 EU AI Act💵 Value / ROI module
Step 1 · Intake
The system under evaluation
Step 2 · Suite
Evaluation battery
Each dimension is an independent test grounded in a public framework. Retrieval tests auto-enable for RAG systems.
Step 3 · Run
Running evaluation…
Assurance report
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Assurance score
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Tests run
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Critical + high
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Reg. readiness
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Dimension scorecard
Risk findings
Regulatory readiness
Mapping is indicative and supports, but does not replace, a formal examination. Per-test evidence is generated for the working papers.
Value case
Economic impact of remediating to a scalable state. ROI is discounted by measured effectiveness — paper return only counts when the system clears its gates.
Value-adjusted ROI · year one
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Remediation roadmap
Methodology & limitations
Connect
How the platform connects to a company
Five tiers, shallow to deep. Start a customer on the easiest tier that answers their question, then earn access downward as trust builds. Every tier runs read-only and least-privilege by default.
Does it have its own MCP server? Yes.
The platform exposes an MCP server so a customer's own agents (or Claude) can invoke evaluation as a tool — run_evaluation, get_report, list_suites — over authenticated remote transport. It can also act as an MCP client to read a customer's context read-only for testing. MCP is the on-demand agent interface; passive workflow observation uses the gateway and OpenTelemetry tiers below.
Connect a system
Or load a vertical sample:
Used when the method is "Batch upload". Real detectors score correctness, PII disclosure, and toxicity from your data; other dimensions are estimated.
When a runner endpoint is saved and the method is not "Simulated", assessments execute against the live runner instead of the in-browser engine. Start the dev runner with node scripts/local-runner.mjs.