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Enterprise-grade quality control for every step of your AI workflow.

Agents Testing QA

Overview


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Maihem is an enterprise-grade quality control tool for every step of your AI workflow. It empowers technology leaders and engineering teams to test, troubleshoot, and monitor any (agentic) AI workflow at scale.

Key Features:
  • Connect Maihem's flexible AI quality control system to any (agentic) AI workflow
  • Systematically test and monitor the performance of your AI application
  • Effortlessly supervise AI systems and collaborate between team members with Maihem's intuitive no-code interface

    Use Cases:
  • Analyze any AI workflow
  • Catch critical flaws before your users do
  • Easily collaborate across teams

    Benefits:
  • Industry-leading AI testing and red-teaming capabilities at scale
  • Auto-generate diverse, realistic, and dynamic datasets to test your AI
  • Secure data with Maihem's infrastructure and access controls.

    • Capabilities

    • Automates testing of AI applications to ensure performance and safety.
    • Continuously monitors AI application performance using industry-leading eval metrics.
    • Generates thousands of critical edge cases and normal user behaviors to expose LLM vulnerabilities.
    • Simulates thousands of users to test LLM applications before deployment, uncovering potential issues.
    • Evaluates LLM applications using custom performance and risk metrics tailored to specific needs.
    • Generates hyper-realistic simulated data to improve and fine-tune LLM applications.
    • Detects bias and toxic content in AI agent responses to ensure safety and compliance.
    • Assesses agent alignment with company brand messaging and values.
    • Detects leaks of Personally Identifiable Information (PII) to maintain data security.
    • Tests correct function calling and tool use to ensure proper AI functionality.
    • Integrates with AI applications using SDK or API for seamless testing.
    • Executes AI red-teaming to systematically stress-test AI applications and identify vulnerabilities.
    • Challenges agents with contextually relevant questions to assess RAG effectiveness.
    • Detects excessive customer data collection and advisory overreach.
    • Detects if the agent exposes internal system access.
    • Ensures quality of customer interactions and satisfaction by simulating real use cases.
    • Runs rigorous simulations to test AI applications' compliance with regulations such as GDPR and the EU AI Act.
    • Auto-generates diverse, realistic, and dynamic datasets to test AI at scale.
    • Provides human-in-the-loop reviews with an intuitive no-code interface.
    • Generates AI test and compliance reports to facilitate stakeholder management.

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