AtScale, a leading provider of the universal semantic layer for enterprise AI and analytics, today announced AtScale for AI, a suite of capabilities designed to help enterprises define what business questions mean, compute trusted answers, and continuously test and improve AI accuracy. Powered by AtScale’s AI Computation Engine (ACE), the suite brings together AtScale Ava, AtScale CoModel, and AtScale Canary. The launch also introduces a new AtScale website reflecting the company’s expanded focus on trusted enterprise AI.

Enterprise AI has a business logic problem. A question as simple as “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 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 the business already runs on.

“The AI industry keeps telling enterprises to buy a smarter model, but a model can’t reason its way to your definition of revenue,” said Chris Lynch, CEO of AtScale. “Enterprises need to define what an answer means, compute it correctly, and prove it holds up. AtScale for AI brings those capabilities together so accuracy becomes something businesses can measure, improve, and demand.”

AtScale for AI is designed to create a continuous improvement loop across four connected capabilities:

  • AtScale CoModel helps teams use AI to create and maintain semantic models, translating business context into shared definitions of metrics, dimensions, and relationships. Proposed changes remain subject to human review, helping teams keep definitions current as their business and data evolve.

  • AtScale Ava (Ask, Verify, Answer) connects natural-language questions to the intended governed metrics before computation begins. If a user asks about “revenue,” Ava can clarify whether they mean bookings, recognized revenue, or contract value, then pass the resolved request to ACE.

  • 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 to identify where answers go wrong, and track regressions over time.

Together, these capabilities connect semantic modeling, question clarification, computation, and evaluation. Questions handled by Ava and errors identified by Canary can inform improvements through CoModel, with human review governing changes to business definitions and logic.

“In banking, an AI answer that’s almost right is still wrong. The challenge was never model intelligence; it was making sure every agent calculated metrics with precision and accuracy – no exceptions. By building on the governed definitions we already trusted, AtScale gave our agents that context and precision without reinventing the logic behind it: on a 10 TB table in Google Cloud, with AtScale as the semantic layer, accuracy on AI driven deterministic analytics took a quantum leap in performance from 65% to better than 99%,” said Dr. Kevin McLafferty, Head of Data & Analytics, UK Commercial Banking, HSBC.

“AI shouldn’t create a second version of the truth. At Carrefour, we already have a semantic layer governing thousands of KPIs across the business. Extending that foundation to AI means our agents can use the same definitions as our analysts and BI tools, rather than producing different answers depending on how someone asks the question,” said Nicolas Treanton, Data & Analytics Director at Carrefour.

The suite reinforces AtScale’s commitment to an open, independent semantic layer and 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.

In a separate announcement published today, AtScale detailed findings from its evaluation of seven LLMs using BIRD-Interact, showing that adding an AtScale semantic layer improved accuracy across every tested model. The research informs AtScale’s approach to repeatable enterprise AI evaluation and the development of Canary. Full results and methodology are available in the accompanying benchmark announcement.

AtScale Ava and ACE are available today. AtScale CoModel and AtScale Canary are coming soon. AtScale’s newly relaunched website brings the AtScale for AI portfolio and the company’s vision for trusted enterprise AI together at www.atscale.com.

AtScale will be hosting a webinar “Make Your AI Show Its Homework” about the Bird-Interact Benchmark results on October 28, 2026 at 2PM ET. Register here to learn more.

About AtScale:

AtScale helps enterprises turn data into trusted answers for AI and analytics. As a leading provider of the universal semantic layer, AtScale gives enterprises a shared foundation for consistent business metrics, helping them improve AI accuracy, reduce computational costs, and make decisions with confidence. Learn more at www.atscale.com.

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