AtScale Launches AtScale for AI to Improve Enterprise AI Accuracy

AtScale, a leading provider of the universal semantic layer for enterprise AI and analytics, has announced the launch of AtScale for AI. This new suite is designed to help enterprises close the accuracy gap in their artificial intelligence (AI) systems by providing a comprehensive solution for defining business questions, computing trusted answers, and continuously testing and improving AI accuracy.

AtScale’s CEO, Chris Lynch, emphasized that enterprise AI often struggles with a ‘business logic problem.’ A simple question like ‘What was our revenue last quarter?’ requires an agreed definition of revenue, the right reporting period, and a governed calculation. More capable models alone cannot supply business definitions that an enterprise has never made explicit.

AtScale for AI connects natural-language questions to shared business definitions and computation, giving AI agents the same governed metrics that the business already runs on. This approach ensures that AI answers are accurate, consistent, and reliable, rather than producing different results depending on how someone asks the question.

The suite is powered by AtScale’s AI Computation Engine (ACE) and brings together three key capabilities: AtScale Ava, AtScale CoModel, and AtScale Canary. AtScale Ava connects natural-language questions to governed metrics before computation begins, ensuring that users get accurate answers to their business questions. AtScale CoModel helps teams use AI to create and maintain semantic models, translating business context into shared definitions of metrics, dimensions, and relationships.

AtScale ACE applies governed metric definitions and calculation logic to enterprise data, providing consistent results across AI agents, applications, and analytics tools while optimizing query performance and computational cost. AtScale Canary is designed to help enterprises evaluate AI answers against validated results using a framework based on the BIRD-Interact benchmark and their own business question/answer pairs.

The capabilities of AtScale for AI are connected in a continuous improvement loop. Questions handled by Ava can inform improvements through CoModel, with human review governing changes to business definitions and logic. This approach ensures that accuracy becomes something businesses can measure, improve, and demand.

Dr. Kevin McLafferty, Head of Data & Analytics at HSBC UK Commercial Banking, highlighted the importance of governed metrics in AI-driven analytics. He noted that an AI answer that’s almost right is still wrong, and that the challenge was never model intelligence but rather ensuring every agent calculated metrics with precision and accuracy.

Nicolas Treanton, Data & Analytics Director at Carrefour, emphasized the need for a single version of truth in enterprise AI. AtScale’s semantic layer governing thousands of KPIs across the business allows agents to use the same definitions as analysts and BI tools, rather than producing different answers depending on how someone asks the question.

AtScale’s commitment to an open, independent semantic layer is reinforced by its support for Apache Ossie (formerly Open Semantic Interchange), advancing shared semantic standards so organizations can preserve business logic as they adopt new AI models, agents, and data platforms. The company has also published findings from its evaluation of seven LLMs using BIRD-Interact, showing that adding an AtScale semantic layer improved accuracy across every tested model.