First independent LLM risk evaluation service
AI Insights from LatticeFlow AI is the first independent LLM risk evaluation service for secure business adoption, providing AI and governance, risk, and compliance (GRC) leaders with clear, actionable intelligence on enabling fast, secure, and confident adoption of foundation models.
Based on neutrality, precision, and trust, AI Insights addresses growing concern over the lack of transparency, relevance, and independence in today’s leaderboard-driven benchmarks fo LLM. Most rely on static benchmark evaluations or crowdsourced ratings, methods that have often been proven to be gamed and disconnected from real-world enterprise needs.
AI Insights sets a new standard, favouring transparency, independence, and real-world relevance over leaderboard rankings and performance metrics. It’s designed to provide enterprise leaders with independent, trustworthy, and business-oriented evaluations that support the secure and compliant adoption of AI.
“For the first time, AI, risk and compliance leaders can get independent, transparent and technical evidence about whether a foundation model is fit for use before it’s deployed,” said Dr Petar Tsankov, CEO and Co-founder of LatticeFlow AI. “AI Insights enables organisations to accelerate AI adoption by ensuring secure and compliant AI deployment.”
AI Insights provides independent evaluations of foundation models, utilising the most comprehensive set of benchmarks tailored to real-world business requirements, encompassing security, fairness, and regulatory alignment.
Each evaluation offers clear and actionable recommendations to support the secure and compliant adoption of generative AI. The results are delivered in intuitive reports that clarify model behavior, highlight critical issues such as bias or prompt vulnerabilities, and provide recommendations for mitigation.
The launch comes as scrutiny mounts around traditional AI benchmarks, many of which reward models for optimising against leaderboards rather than performing safely and reliably in real-world applications.
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