NTT DATA & Corvic AI partner to unlock enterprise data for AI
NTT DATA has entered into a strategic alliance with Corvic AI to offer adaptive AI solutions to enterprise clients in the US and UK aimed at improving the readiness of complex data for generative artificial intelligence applications.
The partnership will see NTT DATA integrate Corvic AI's data platform into its consulting services, addressing a critical barrier to enterprise adoption of generative AI where fragmented and multimodal data formats hinder operational deployment.
Addressing data fragmentation
Many enterprise initiatives involving generative AI struggle to reach production environments, not due to inefficiencies in AI models but as a result of underlying data issues. Documents, tables, images, and varied legacy systems often store organisational data in fragmented and incompatible formats, posing challenges for AI-driven knowledge management.
Corvic AI's platform has been developed to recognise, reconcile, and integrate information from a range of data sources, even when the terminology and formats differ. The platform supports in-depth analysis by converting disparate data into structured, AI-ready formats, and features an adaptive orchestration engine capable of operating in dynamic business environments such as post-merger consolidations and multi-entity financial governance.
Deployment in practice
One example of the partnership in action is seen with a global beverage manufacturer, where Corvic AI's data mapping and migration technology facilitated the optimisation of product placement and pricing strategies. This deployment led to revenue growth and uncovered additional revenue opportunities, while the solution's self-learning features allowed it to adjust to fluctuations in product inventory automatically.
NTT DATA now provides Corvic AI's platform to clients in the US and UK, with intentions to expand availability in other markets as demand grows.
Executive perspectives
"Our clients want to tap the vast potential locked away in troves of complex data, but it's typically stored in disparate formats and locations across the enterprise," said Wendy Collins, Chief AI Officer, NTT DATA North America. "Corvic AI's technology cuts through the noise with AI-powered data mapping and migration that ultimately empowers clients to drive innovation and transform operations."
Farshid Sabet, CEO of Corvic AI, highlighted the shift in focus from model development to data infrastructure as fundamental for realising the benefits of enterprise AI.
"AI models have matured, but the underlying data infrastructure hasn't kept up," said Farshid Sabet, CEO, Corvic AI. "By partnering with NTT DATA, we're helping enterprise teams move from siloed information to orchestrated intelligence and doing it at a pace that fits real business demands. This alliance is about bridging that gap."
Technical capabilities
The Corvic AI platform includes tools for multimodal data integration, seamless enterprise integration, and is designed for dynamic environment readiness. The platform's core features allow it to deliver reusable and extendable solutions, supporting more rigorous analytics and enabling faster decision-making with reduced operational costs.
Its adaptive orchestration engine is tailored for scenarios where legacy systems and incomplete data sets prevail, providing a scalable foundation for organisations to implement generative AI with minimal disruption to existing IT infrastructure.
Market and expansion
According to NTT DATA, the alliance responds to a growing need among enterprise clients for solutions that can efficiently transform their current data ecosystems and unlock the value in large, unstructured data sets. The company serves a significant portion of Fortune Global 100 companies and expects that integrating Corvic AI technology will support these clients in maximising their AI investments.
The alliance builds on the organisations' commitment to developing solutions that support clients through phases of digital transformation and operational optimisation, particularly where complex data landscapes are the norm.
