iLink Digital Partners With DataGaps To Build Trusted AI-Ready Data Foundations
The companies will help enterprises identify data issues proactively, improve compliance, and ensure business-critical information remains accurate and reliable.
iLink Digital, an AI-first enterprise transformation partner, has announced a strategic partnership with DataGaps, a provider of AI-powered data observability, testing, validation, and quality engineering solutions.
The collaboration brings together iLink Digital’s expertise in data modernisation, cloud, analytics, and AI transformation with DataGaps’ capabilities in automated data testing, continuous monitoring, validation, and observability.
The companies will help enterprises identify data issues proactively, improve compliance, and ensure business-critical information remains accurate and reliable.
By integrating intelligent observability with modern data platforms, the partnership aims to help organisations gain visibility across data pipelines, reduce operational risks caused by inconsistent data, and move from reactive data management to continuous quality assurance.
"Organisations are increasingly recognising that successful AI initiatives depend on the quality, reliability, and governance of the data behind them. Our partnership with DataGaps brings together complementary strengths in data modernization and data quality engineering, enabling customers to build trusted data ecosystems that support innovation, accelerate decision-making, and drive measurable business outcomes," Sakthi Kannan, iLink Digital CEO, Data & AI, said.
"Data observability and quality are no longer optional in today's data-driven enterprises. By partnering with iLink Digital, we can help organizations gain greater confidence in their data, improve operational resilience, and create a stronger foundation for analytics and AI initiatives at scale," Narendar Yalamanchilli, DataGap Founder & CEO, added.
The partnership is part of iLink Digital’s broader AI-first strategy to help enterprises move beyond AI experimentation and achieve production-scale outcomes. By combining data quality engineering with AI, analytics, and governance capabilities, the companies aim to enable scalable and reliable AI programmes.
