Many Fortune 500 companies and other large enterprises depend on clean, reliable data to run their businesses. But most still rely on a patchwork of tools and large teams of outsourced data engineers to operate, maintain, and remediate issues with their data pipelines. Because of the complexity and scale of these tools and services, bugs and errors are often slow to fix, delaying critical decisions and potentially putting the business at risk. In an environment transformed by applied AI, where data-driven insights about business operations are one of the few remaining competitive advantages, this pain point can quickly become existential.

Pantomath is fundamentally changing this paradigm by building an AI agent that automatically maintains complex enterprise data pipelines. The Pantomath team sees an opportunity to build a one-stop shop for enterprises currently cobbling together a fragmented set of software and services providers. Enterprises don't need yet another tool for humans to do this work. They need the work done. That's why we're leading Pantomath's $30 million Series B round.

The Vision for Agentic Data Engineering

As the first wave of data operations companies scaled, each offered a new point solution to resolve a specific challenge. The result has been a complex web of data operations systems, which span data observability, data quality, data catalog, and extract, transform, load (ETL) processes. Each is necessary, but none are sufficient on their own, creating friction and requiring manual oversight.

Applying AI to the challenge of automating complex enterprise data engineering requires agentic solutions with full context of how data moves throughout the business and its systems. The models also need to know how that data is used across the enterprise in real time. Pantomath's platform does exactly this: it pinpoints failures in the operations flow of the data pipeline, helping human data teams, and now AI agents, get to the root cause of data quality issues surfaced by other tools in the stack.

Take the case of a sales dashboard showing outdated revenue metrics. Pantomath approaches this from the operational observability angle, which easily pinpoints the root cause, such as a delayed ETL job. Their pipeline traceability then reveals the upstream jobs or datasets affected, enabling data agents to efficiently resolve the problem and mitigate its impact. Contrast this with today’s data observability products, which can only flag the dashboard issue as one of data freshness, requiring significant time for manual problem diagnosis and resolution.

Pantomath’s AI agents unlock immediate value by resolving basic but urgent tasks like debugging business intelligence dashboards and dramatically reducing time to resolution. By freeing up time for human data teams to focus on higher-impact analysis, they compound value across the enterprise.

Decades of Enterprise Data Expertise

When brothers Shashank and Somesh Saxena first approached us with their idea of building an AI agent that does the work of enterprise data operations, we sensed natural alignment with our applied AI thesis. But it was their deep understanding of the real day-to-day operational pain points that enterprises face that solidified our conviction.

They both have personal insights into the multitude of platforms and services that run Fortune 500 data stacks, having led data and analytics teams for companies including Citi, Kroger, and GE. Shashank also brings successful entrepreneurial experience as a second-time founder, having sold his first company, VNDLY, to Workday. This combination of real-world enterprise expertise and founder experience sets the foundation for their success.

Transforming Enterprise Data Operations

Investing in Pantomath represents more than supporting a fast-growing startup with an experienced team. We’re backing a vision to reimagine critical data workflows at the heart of the world's largest businesses. As applied AI continues to reshape software engineering, Pantomath is extending this power to data teams, enabling them to finally focus less on debugging and more on data-driven decision making.

Shashank, Somesh, Narayan, and the Pantomath team are building a future where data engineers focus on insights, not infrastructure maintenance. The team combines the know-how of industry veterans with the nimbleness and creativity that it takes to stay ahead in a competitive market. We look forward to partnering with them as they realize our shared vision of automating data operations and transforming enterprise data engineering.