Dashboard
Independent AI evaluation & assurance
Overview

Prove any AI system works, independently.

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
0
none yet
Avg assurance
across systems
Open findings
0
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

Assurance score
Tests run
Critical + high
Reg. readiness
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
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.
    The integration ladder
    Seamless & compliant by default
    🔐 SSO via OIDC / SAML 👁️ Read-only, least-privilege 🚫 Zero-retention option 🏢 VPC / single-tenant deploy 🩹 PII / PHI redaction at ingest 📄 SOC 2 · BAA ready
    See docs/CONNECTION-ROADMAP.md in the repo for the full phased plan, timelines, and security model.