Maihem

What

Maihem is an AI quality assurance, testing, monitoring, and red-teaming platform for organizations deploying AI applications. It is positioned as an enterprise-focused system that helps teams evaluate performance, safety, security, and user-experience risks before and after deployment.

The workflow shown is: connect an AI app via SDK/API, configure evaluator modules to match quality standards, then test and monitor behavior using simulations, generated test data, and reporting. The product appears to serve AI, product, and risk/governance teams that need structured, repeatable evaluation across multiple model providers.

Features

• Modular AI evaluation framework: Includes focused modules (e.g., RAG, agentic workflows, CX, bias, toxicity, overreach, privacy/PII, system access) so teams can test specific failure modes instead of running only generic checks.
• RAG and agent behavior testing: Evaluates answer relevance, context relevance, hallucination, tool use, and goal achievement to validate both retrieval quality and agent execution reliability.
• Safety and brand risk testing: Assesses bias categories, toxic outputs, competitor recommendations, and negative sentiment to identify reputational and policy risks in customer-facing AI.
• Privacy and security checks: Tests handling of sensitive data (e.g., DOB, financial details, contact info, government IDs, health information) and potential prompt leakage/system exposure.
• Test data generation and simulation monitoring: Auto-generates diverse datasets and uses simulation-based monitoring to check robustness as models or prompts change over time.
• Operational enablement for enterprises: Offers no-code human-in-the-loop reviews, automated test/compliance reporting, SDK/API integration, and an LLM-agnostic approach across major and open-source models.

Helpful Tips

• Map modules to real risk owners: Align each module (e.g., privacy, brand reputation, bias) to internal stakeholders such as legal, security, CX, and product to speed remediation decisions.
• Prioritize high-impact failure paths first: Start with scenarios most tied to customer harm or regulatory exposure, then expand coverage to broader quality dimensions.
• Use both pre-launch and production cycles: Treat evaluation as continuous; run baseline tests before release and repeat after model, prompt, or tool-chain updates.
• Define pass/fail thresholds early: Agree on measurable acceptance criteria per module so reports can drive release gates rather than only informational dashboards.
• Validate enterprise claims during procurement: The site states strong security posture and custom enterprise options; confirm architecture, controls, and deployment fit during technical due diligence.