Trust3 AI announced it has achieved a significant AI Accuracy benchmark, a breakthrough in enhancing the precision and reliability of enterprise AI systems. This achievement comes from embedding comprehensive business context into AI-powered analytics and business intelligence platforms.
The new context engine addresses a critical challenge facing chief information officers, chief technology officers, and chief data officers: traditional text-to-SQL and AI business intelligence systems frequently deliver inconsistent or inaccurate results because they lack essential business semantics, governance rules, and domain relationships that give enterprise data meaningful context.
“To make AI truly useful across the enterprise, we must stop treating context as a one-off effort and instead build a unified, reusable semantic foundation,” said Neeraj Sabharwal, co-founder of Trust3 AI. “Trust3 IQ is that foundation — the missing layer between natural language and data systems that unlocks consistent, governed, accurate insights.”
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Trust3 IQ delivers several key capabilities that differentiate it from existing solutions. The platform centralizes business semantics and metric logic, capturing mappings between technical database structures and meaningful business concepts. It models semantic relationships and hierarchies across organizational silos through a knowledge graph architecture, enabling AI systems to reason across domains even when data exists in different platforms.
The solution also embeds governance, privacy, and compliance logic directly into the context engine, ensuring AI-derived insights automatically respect enterprise policies. Rather than overwhelming systems with complete semantic models, IQ dynamically selects only relevant context portions, addressing token limits and performance bottlenecks while maintaining accuracy.
In benchmark testing using the BIRD evaluation framework across 95 databases in 37 domains, Trust3 IQ demonstrated superior performance compared to existing enterprise solutions. For simple queries, IQ achieved approximately 62.5% accuracy versus Snowflake’s 37.5% and Databricks Genie’s 55.6%. Performance advantages were even more pronounced with complex queries, where IQ maintained 50% accuracy compared to 20% for Snowflake and 35.7% for Genie.
Early enterprise adopters report accelerated time-to-insight, fewer contextual errors, and significantly reduced manual rework across finance, operations, and analytics departments. One global enterprise reduced semantic modeling effort by over 60% while enabling a unified AI-powered analytics layer spanning multiple data platforms.
The platform connects natively to major data platforms including Snowflake, Databricks, BigQuery, and PostgreSQL, with semantic definitions created once and reused across platforms. Any AI agent, retrieval-augmented generation system, or analytics engine can access the context engine through APIs or Model Context Protocol servers.
Trust3 IQ is now available to enterprises through subscription and on-demand pilot programs. Organizations interested in exploring the solution can access live demos and technical documentation through Trust3’s onboarding toolkit.
Every company, including industry leaders like Salesforce (Tableau), Snowflake, Microsoft (PowerBI), and Databricks, is actively working to address this problem. Salesforce recently introduced the AI Trust Layer as part of their Data Cloud, highlighting the importance of trust and context in managing and utilizing enterprise data. However, this problem cannot be fully resolved within the confines of individual data platforms.
The complexity and diversity of enterprise data ecosystems demand an external, universal context engine that can operate across platforms and systems, providing a unified, scalable solution. Trust3 IQ addresses this critical need by enabling seamless integration and context standardization across various platforms and data sources.
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