DataBahn has raised $40 million in Series B funding to expand its platform for managing enterprise data in the age of AI. The round was led by Insight Partners, with participation from existing investors Forgepoint, GTM Capital, and S3 Ventures, bringing the company’s total funding to $59 million.

The investment reflects growing demand for infrastructure that does more than move data between systems. As enterprises deploy AI agents, copilots, and autonomous applications, they increasingly need platforms that can govern, enrich, and deliver trusted data while keeping infrastructure costs under control.

Beyond Data Pipelines

Traditional enterprise data pipelines were built to transfer information from one application to another. That model worked well for analytics and reporting, but AI workloads introduce new requirements.

Large language models and AI agents depend on timely, contextual enterprise data to generate accurate responses and automate decisions. At the same time, organizations are grappling with rising cloud egress fees, growing storage requirements, increasing inference costs, and expanding data volumes.

DataBahn argues that these pressures require a different architectural approach. Rather than simply transporting data, its platform is designed to govern, orchestrate, and activate enterprise data across applications, cloud environments, and AI models. The company describes this emerging layer as an “agentic data control plane”—an architectural approach intended to reduce unnecessary data movement while providing AI systems with relevant business context when needed.

According to DataBahn, the platform continuously filters, enriches, and routes only the data required for specific workloads, helping organizations reduce storage, compute, token consumption, and cloud data transfer costs while improving the quality of information available to AI applications.

Building Infrastructure for Enterprise AI

The funding will primarily support research and development as DataBahn expands product capabilities for organizations ranging from large global enterprises to mid-sized businesses.

The company says it has focused on building a platform capable of meeting the scalability, security, and governance requirements of highly regulated industries. Its customer base includes Fortune 100 organizations across healthcare, financial services, manufacturing, and transportation.

DataBahn has also adopted a partner-led go-to-market strategy, working through channel and strategic partners rather than relying solely on direct sales. The company reports strong business momentum, including significant year-over-year revenue growth, high customer retention, and continued expansion within existing enterprise accounts, although these figures were provided by the company and have not been independently verified.

CEO and co-founder Nanda Santhana said the next generation of enterprise infrastructure will be defined less by where data resides and more by how intelligently it is orchestrated for both traditional applications and AI systems. The company’s goal is to help enterprises establish a common data layer that supports AI adoption while lowering the operational costs associated with moving and processing large datasets.

AI Is Reshaping Enterprise Data Architecture

The funding announcement comes amid broader changes in enterprise data management. As organizations expand AI initiatives, industry analysts have increasingly pointed to fragmented data architectures and inconsistent governance as barriers to successful AI deployment.

Instead of replicating data across multiple platforms, enterprises are exploring architectures that provide secure, governed access to information wherever it resides. This aligns with growing interest in AI-enabled data fabrics and other approaches that emphasize data orchestration, governance, and contextual access over large-scale data duplication.

DataBahn’s latest funding suggests investors see opportunity in this evolving infrastructure layer. While the market for AI data platforms is becoming increasingly competitive, enterprises continue searching for ways to balance AI performance with governance, security, and cloud cost optimization.

As AI agents become more deeply integrated into enterprise workflows, platforms that can intelligently manage data in motion may become as important as the models consuming that data. DataBahn is betting that the future of enterprise AI will depend not only on better models, but also on smarter data orchestration.