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Learn More<\/em><\/p>\n\n\n\n Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding exactly how data moves through complex systems.<\/p>\n\n\n\n The company\u2019s new Data Journeys platform, announced today, addresses a critical blind spot for organizations implementing AI \u2014 tracking not just where data resides, but how and why it\u2019s being used across applications, cloud services, and third-party systems.<\/p>\n\n\n\n \u201cThe fundamental premise is making sure that our customers have this AI native, context-aware view, very visual view of the entire journey of data across their applications, services, infrastructures, third parties,\u201d said Abhi Sharma, CEO and co-founder of Relyance AI, in an exclusive interview with VentureBeat. \u201cYou can really get at the heart of the why of data processing, which is the most foundational layer needed for general AI governance.\u201d<\/p>\n\n\n\n The launch comes at a pivotal moment for enterprise AI governance. As companies accelerate AI implementation, they face mounting pressure from regulators worldwide. More than a quarter of Fortune 500 companies have identified AI regulation as a risk in SEC filings, and GDPR-related fines reached \u20ac1.2 billion\u00a0in 2024 alone (approximately $1.26 billion at current exchange rates).<\/p>\n\n\n\n The platform represents a significant evolution from conventional data lineage approaches, which typically track data movement on a table-to-table or column-to-column basis within specific systems.<\/p>\n\n\n\n \u201cThe status quo for data lineage is basically table to table and column level lineage. I can see how data moved within my Snowflake instance or within my S3 buckets,\u201d Sharma explained. \u201cBut nobody can answer: Where did it come from originally? What nuanced transformation happened between data pipelines, third-party vendors, API calls, RAG architectures, to finally land up here?\u201d<\/p>\n\n\n\n Data Journeys aims to provide this comprehensive view, showing the complete data lifecycle from original collection through every transformation and use case. The system starts with code analysis rather than simply connecting to data repositories, giving it context about why data is being processed in specific ways.<\/p>\n\n\n\n Lawrence Schoeb, senior director and DPO at Samsara, one of Relyance\u2019s customers, said in a statement, \u201cThe automated, context-aware data lineage capabilities would address our most pressing challenges. It represents exactly what we\u2019ve been looking for to support our global AI governance framework.\u201d<\/p>\n\n\n\n According to Sharma, Data Journeys delivers value in four critical areas:<\/p>\n\n\n\n First, compliance and risk management: \u201cToday, you kind of are required to vouch for integrity of data processing, but you can\u2019t see inside. It\u2019s basically blind governance,\u201d Sharma said. The platform enables organizations to prove the integrity of their data practices when facing regulatory scrutiny.<\/p>\n\n\n\n Second, precise bias detection: Rather than just examining the immediate dataset used to train a model, companies can trace potential bias to its source. \u201cBias often happens at inference time, not because you had bias in the dataset,\u201d Sharma noted. \u201cThe point is, it\u2019s actually not that dataset. It\u2019s the journey it took.\u201d<\/p>\n\n\n\n Third, explainability and accountability: For high-stakes AI decisions like loan approvals or medical diagnoses, understanding the complete data provenance becomes essential. \u201cThe why behind that is super important, and many times, the incorrect behavior of the model is completely dependent on the multiple steps it took before the inference time,\u201d Sharma explained.<\/p>\n\n\n\n Finally, regulatory compliance: The platform provides what Sharma calls a \u201cmathematical proof point\u201d that companies are using data appropriately, helping them navigate increasingly complex global regulations.<\/p>\n\n\n\n Relyance claims the platform delivers measurable returns on investment. According to Sharma, customers have seen 70-80% time savings in compliance documentation and evidence gathering. What he calls \u201ctime to certainty\u201d\u2014the ability to quickly answer questions about how specific data is being used\u2014has been reduced from hours to minutes.<\/p>\n\n\n\n In one example Sharma shared, a direct-to-consumer company was switching payment processors from Braintree to Stripe. An engineer working on the project inadvertently created code that stored credit card information in plain text under the wrong column name in Snowflake.<\/p>\n\n\n\n \u201cWe caught that at the time the code was checked in,\u201d Sharma said. Without Data Journeys\u2019 visual representation of data flows, this potential security incident might have gone undetected until much later.<\/p>\n\n\n\n Alongside Data Journeys, Relyance is introducing InHost, a self-hosted deployment model designed for organizations with strict data sovereignty requirements or those in highly regulated industries.<\/p>\n\n\n\n \u201cThe industries that are most interested in the in-host option are more regulated industries \u2014 FinTech and healthcare,\u201d said Sharma. This includes banking, fraud detection, credit worthiness applications, genetics, and personal healthcare services.<\/p>\n\n\n\n The flexibility to deploy either in the cloud or within a company\u2019s own infrastructure addresses growing concerns about sensitive data leaving organizational boundaries, particularly for AI applications that might process regulated information.<\/p>\n\n\n\n Relyance is positioning Data Journeys as part of a broader strategy to become what Sharma calls \u201ca unified AI-native platform\u201d for global privacy compliance, data security posture management, and AI governance.<\/p>\n\n\n\n \u201cIn the second half of this year, I\u2019m launching an AI governance solution which will be a 360-degree management of all AI footprint in your environment,\u201d Sharma revealed, encompassing compliance, real-time ethics monitoring, bias detection, and accountability for both third-party and in-house AI systems.<\/p>\n\n\n\n The company\u2019s long-term vision is ambitious. \u201cAI agents are going to run the world, and we want to be that company that provides the infrastructure for organizations to trust and govern it,\u201d Sharma said. \u201cWe want to help improve the data utility index of the world.\u201d<\/p>\n\n\n\n Relyance faces competition from established players in adjacent spaces. In an earlier interview with TechCrunch, Sharma acknowledged competitors including OneTrust, Transcend, DataGrail, and Securiti AI, though he emphasized that Relyance\u2019s integrated approach sets it apart.<\/p>\n\n\n\n Investors seem convinced of the company\u2019s potential. Its $32.1 million Series B round in October 2024, led by Thomvest Ventures with participation from Microsoft\u2019s M12 Ventures Fund, brought Relyance\u2019s total funding to $59 million.<\/p>\n\n\n\n Umesh Padval, Managing Director at Thomvest Ventures, highlighted the urgency of the problem Relyance is solving: \u201cRelyance AI empowers Chief Privacy, Security, and Information Officers to manage data privacy and compliance, avoiding costly penalties while driving safe and responsible AI adoption.\u201d<\/p>\n\n\n\n Sharma framed the company\u2019s mission as part of a broader imperative for organizations implementing AI technologies.<\/p>\n\n\n\n \u201cAI is becoming kind of the default imperative in your organization, and everybody needs to think about that core, foundational pillar in your organization, which is going to be the infrastructure for trust and governance,\u201d he said.<\/p>\n\n\n\n \u201cWhether leaders use Relyance or not, it is an important aspect to think about, because that will really unlock how fast you can get AI adoption in a meaningful way within an organization.\u201d<\/p>\n\n\n\n As enterprises rush to implement AI, the ability to maintain visibility into data processes has evolved from a mere compliance checkbox to a fundamental business necessity. This shift represents one of those quiet but profound changes that doesn\u2019t make headlines but reshapes industries. Companies building these visibility tools are essentially creating the air traffic control systems for AI\u2014not the flashy jets themselves, but the infrastructure that prevents them from crashing into each other. Without it, even the most impressive algorithms become corporate liabilities.<\/p>\n Daily insights on business use cases with VB Daily<\/strong><\/p>\n If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.<\/p>\n \n\t\t\t\t\tThanks for subscribing. Check out more VB newsletters here.\n\t\t\t\t<\/p>\n An error occured.<\/p>\n<\/p><\/div>\n
\n<\/div>How Data Journeys tracks information flow where others fall short<\/h2>\n\n\n\n
Four business problems that data visibility promises to solve<\/h2>\n\n\n\n
From hours to minutes: Measurable returns on better data oversight<\/h2>\n\n\n\n
Keeping sensitive data inside your walls: The self-hosted option<\/h2>\n\n\n\n
Relyance AI\u2019s expansion plans point to growing AI governance market<\/h2>\n\n\n\n
Investors bet big on data governance as competition heats up<\/h2>\n\n\n\n
Why data oversight might determine AI success in the enterprise<\/h2>\n\n\n\n
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