AI System Testing

Structured validation for bias, hallucinations, safety, and performance - so AI moves to production with evidence, not assumptions.

Systematic testing protocols built from 5+ years of enterprise AI audits

Identifies risks across fairness, accuracy, safety, and reliability

Generates audit-ready evidence for compliance and stakeholder review

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Fairness

94%
Accuracy
87%
Safety
91%
Bias
75%
Reliability
92%
Robustness
88%

Trusted by the world's most innovative companies

THE REALITY

Most AI validation happens too late—or not at all.

Teams ship models with manual spot-checks, hope-based testing, and incomplete coverage. Issues surface in production, in customer complaints, or in regulatory inquiries—not in structured pre-deployment reviews.

Testing gaps

• No structured test protocols
• Inconsistent evaluation criteria
• Testing squeezed before launch
• Blind spots in edge cases
• Tests don't repeat across versions

AI systems change faster than testing processes can keep up.

Hidden failure modes

• Hallucinations: High
• Demographic bias: High
• Prompt injection vulnerabilities: Critical
• Performance degradation: Medium
• Unsafe content generation: Critical

Failures don't announce themselves—they emerge under real-world conditions.

Evidence gaps

• "How do we know this model is fair?"
• "What testing was done before launch?"
• "Is there an audit trail?"
• "What happens when regulators ask?"

Governance needs evidence. Most testing produces opinions.

THE CAPABILITY

Structured testing that produces evidence, not just results.

AI System Audit applies proven testing protocols across bias, safety, accuracy, and performance—generating the documentation governance and compliance teams need.

• Bias & Fairness Testing
• Hallucination & Accuracy Testing
• Safety & Security Testing
• Performance & Reliability Testing

Validate fairness across demographics and contexts

• Systematically tests for demographic bias, contextual unfairness, and disparate impact across protected attributes and user segments.
• Demographic parity
• Unequal outcomes across groups
• Contextual bias
• Unfair responses based on scenario
• Intersectional analysis
• Compounding biases across attributes
• Language fairness
• Bias in multilingual outputs

How It Works

Connect. Scan. Surface.

Three steps from blind spots to complete visibility.

01

Active Connections

• Cloud: AWS, GCP
• Code: GitHub, GitLab
• Data: Snowflake, BQ
• SaaS: Slack, Notion

Integrate your infrastructure

Read-only API access to your cloud, code, and data platforms.

02

📦 Model artifacts
🔄 API usage patterns
🎯 Inference endpoints
🤖 Agent workflows
💬 Prompt traces

Automated, continuous detection

AI Discovery runs in the background, identifying new systems the moment they deploy.

03

Unified AI Inventory

• GPT-5 Chatbot: Low risk
• Facial Recognition: High Risk
• Financial Analysis Agent: Medium Risk
• Customer Data Agent: Medium Risk

Every discovery flows into your inventory

No manual entry. No spreadsheets. Just instant visibility across your entire AI landscape.

Enterprise AI Governance That Actually Works

Join the organizations that turned governance from a blocker into an enabler. Full visibility, continuous risk testing, and compliance proof — on autopilot.

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