en.Wedoany.com Reported - Data observability and agentic data management company Acceldata officially launched its autonomous data and AI platform for the agentic AI era on May 19 at the Informatica World 2026 conference in Las Vegas. The company simultaneously announced the xLake inference engine, the Agentic Data Management fully autonomous data operations system, and an AI agent data access framework based on the Model Context Protocol (MCP). In an exclusive interview with CRN during the conference, Acceldata Founder and CEO Rohit Choudhary systematically articulated his core judgment on the future direction of enterprise data architecture: "AI agents are breaking the cloud computing centralization model. We are at a historic inflection point, transitioning from the lakehouse era to the post-lakehouse era."
Choudhary pointed out that the lakehouse model built around Snowflake and Databricks once represented a major advancement in enterprise data strategy, but it introduced a new challenge—data silos were not eliminated, merely relocated within individual cloud platforms. "It's 2026, and AI agents are poised to enter production environments at scale. These agents require direct access to enterprise data distributed across cloud, on-premises, and sovereign environments, and they demand verifiable data trustworthiness to make high-stakes inferential decisions." He stated that enterprises can neither migrate all their data into a single giant lakehouse nor should they continue to follow a centralized architectural approach. Acceldata's autonomous data and AI platform is designed precisely to resolve this dilemma. Built on the xLake inference engine, the platform allows agents and analytical tasks to run securely wherever enterprise data resides, delivering business outcomes regardless of data location.
The platform's technical architecture includes several key components. The xLake inference engine serves as the computational foundation of the entire platform, supporting unified inference scheduling across hyperscale clouds, data clouds, and on-premises systems. First unveiled in February 2026, it entered full general availability at this conference. The Agentic Data Management system is responsible for making data operations themselves autonomous; its autonomous agents can automatically detect anomalies, diagnose root causes, generate remediation plans, and execute them, while retaining a human-in-the-loop mechanism to ensure any sensitive decisions undergo human review. The integration between Acceldata and ServiceNow directly connects data quality monitoring with AI workflows—once Acceldata's data quality engine is integrated into the ServiceNow Workflow Data Fabric ecosystem, enterprises can automatically obtain data quality scores and anomaly alerts within the operational chain of AI agents, shortening the closed-loop time from issue detection to remediation. The platform also supports the MCP protocol, allowing any MCP-compatible AI agent to securely access enterprise data through standardized interfaces, eliminating the need for custom integrations for each data source.
Founded in 2018 and headquartered in Campbell, California, Acceldata was co-founded by CEO Rohit Choudhary, CTO Ashwin Rajeeva, Director of Engineering Raghu Mitra Kandikonda, and Senior Architect Gaurav Nagar. All four founders hail from big data infrastructure companies like Hortonworks, possessing over a decade of engineering experience in enterprise-grade data pipelines, distributed systems, and data governance.
Regarding funding and clients, Acceldata has raised over $100 million to date, with an investor lineup including Insight Partners, March Capital, Lightspeed, Sorenson Ventures, and Emergent Ventures. The company's client roster includes globally renowned enterprises such as Dun & Bradstreet, Verisk, Oracle, PubMatic, Walmart-owned PhonePe, DBS Bank, and Qualcomm.
In its data observability market forecast report released at the end of 2025, IDC noted that the market is in a rapid expansion cycle, projected to reach $5.12 billion by 2028. The report also emphasized that as AI agent deployment moves comprehensively from the experimental phase into production environments, enterprise requirements for data trustworthiness and data pipeline health are undergoing a qualitative change—traditional passive monitoring can no longer meet the real-time data quality assurance needed for autonomous agent decision-making. This is precisely the market gap targeted by Acceldata's platform upgrade.
Acceldata's partnership with VAST Data offers a low-risk migration path for enterprises facing Hadoop modernization pressures. The joint solution allows enterprises to seamlessly migrate data and analytical workloads from traditional Hadoop environments to an AI-native unified platform, avoiding the business disruption and high costs associated with traditional "rip-and-replace" migration approaches. This solution has entered the joint customer validation phase.
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