Academic validation meets enterprise scale for next-generation retrieval and orchestration systems

Corvic AI, creator of the world’s first Intelligence Composition Platform (ICP), announced that its benchmarking research paper has been accepted for presentation at ACM AIWare 2025 in South Korea this November. The milestone underscores Corvic’s role in advancing multimodal enterprise intelligence systems that operate reliably at production scale.

The peer-reviewed paper, titled “When Retrieval Finally Works: Benchmarking the Next Era of Enterprise AI,” demonstrates how Corvic’s compositional architecture delivers up to 23% higher answer accuracy than traditional retrieval-and-orchestration stacks—at multi-million-document scale, without data migration or schema refactoring. The paper will be presented at ACM AIWare 2025 by Corvic’s Chief Technology Officer, Dr. Donald Nguyen.

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“For years, enterprises accepted that most AI systems would never reach production-level accuracy,”” said Farshid Sabet, CEO of Corvic AI. “This acceptance by ACM underscores that context accuracy, not model size, is the true differentiator in enterprise performance. Our research demonstrates that AI systems can finally operate reliably at scale, using the data organizations already have.”

The paper’s acceptance at one of the world’s most respected academic venues provides external validation for Corvic’s technology, which has already gained traction with enterprise partners and cloud ecosystems such as Snowflake. The recognition reinforces the company’s dual trajectory, from scientific credibility to enterprise adoption, and highlights the growing importance of open, peer-reviewed benchmarking in the rapidly evolving AI landscape.

At ACM AIWare 2025, Corvic will present its benchmarking methodology for evaluating intelligence systems across PDFs, tabular data, visual content, and complex enterprise datasets, along with a framework defining progressive maturity levels in enterprise AI composition. The work represents a first step toward transparent, repeatable standards for next-generation intelligence systems.

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