DataBahn Raises $40M Series B for an Agentic Data Control Plane

DataBahn closed a $40M Series B led by Insight Partners, with existing investors Forgepoint Capital, GTM Capital, and S3 Ventures participating. The July 30, 2026 round brings the Dallas-area enterprise data company's total funding to $59M and provides fresh capital to expand what it calls an agentic data control plane.

The financing matters because enterprise AI is turning data movement into a control problem. Security platforms, analytics systems, warehouses, copilots, and autonomous agents all need trusted context, while storage, compute, inference, and cloud-transfer costs continue to rise. DataBahn's wager is that the winning infrastructure layer will not simply move more information. It will decide which data should move, improve it in transit, and make the rest available only when an application or agent actually needs it.

What Happened

According to the official Series B announcement, Insight Partners led the $40M round, and DataBahn's existing backers returned. The company previously announced a $17M Series A in June 2025, led by Forgepoint Capital with S3 Ventures and GTM Capital participating. DataBahn says the new financing brings its cumulative funding to $59M.

DataBahn plans to direct the capital toward R&D and product innovation while expanding the platform for customers ranging from global enterprises to mid-market businesses. The company did not disclose a valuation, board changes, or a detailed allocation of proceeds. That leaves the strategic direction clearer than the capitalization details: deepen the product, broaden the market, and extend an early security-data wedge into a larger enterprise control layer.

Why DataBahn's Control-Plane Thesis Matters

Traditional pipelines are effective at moving data from one source to another, but modern enterprise systems demand more than delivery. Data often needs to be reduced, normalized, enriched, governed, and routed differently depending on whether the destination is a SIEM, data lake, analytics engine, or AI agent. DataBahn says its platform makes those decisions while data is in motion and retrieves additional context on demand instead of copying everything into another costly destination.

That distinction matters because AI amplifies the cost of poor data architecture. A model can reason only from the context it receives, and fragmented schemas or unnecessary telemetry make both inference and operations more expensive. DataBahn's thesis is practical: improve the data before downstream systems pay to store, process, and interpret it, while preserving the enterprise's flexibility to decide where the underlying information ultimately resides.

The Metrics Behind the Series B

DataBahn reports more than 400% year-over-year revenue growth, 180% net revenue retention, zero customer churn, and a 97% proof-of-concept win rate. It also says the platform powers enterprise data operations for Fortune 100 organizations across healthcare, financial services, manufacturing, and transportation. These are company-reported metrics rather than independently audited figures, but together they explain why a growth investor would see more than a technical experiment.

The numbers suggest a company converting a difficult infrastructure problem into repeatable enterprise demand. Retention above 100% indicates existing customers are expanding their spending, while a high proof-of-concept win rate suggests the product consistently demonstrates value before full deployment. Zero reported churn raises the standard for future performance, but for this round it supports DataBahn's argument that controlling telemetry cost and complexity is becoming a durable operating requirement.

Leadership and Investor Conviction

Nanda Santhana, DataBahn's CEO and co-founder, frames the next generation of enterprise infrastructure around orchestrating the right data at the right time. Nithya Nareshkumar, the company's president and co-founder, brings experience from regulated financial-market infrastructure and is part of a leadership team spanning security, data, product, and engineering. DataBahn's official leadership page does not list a CTO, so the company's verified technical leadership is represented through its product and engineering executives rather than an inferred title.

Insight Partners adds a growth-stage signal to a syndicate already familiar with the company. Max Wolff, a Managing Director at Insight, said enterprise leaders describe DataBahn as foundational to their future security architecture. Forgepoint, GTM Capital, and S3 Ventures returning for the Series B is also meaningful because existing investors had direct visibility into the company's performance between rounds and chose to support a broader strategic mandate.

From Security Data Pipelines to Enterprise AI Infrastructure

DataBahn emerged by solving a specific security challenge: enterprises were collecting more telemetry than their pipelines and budgets could reasonably handle. In its founder essay on the Series B, Nanda Santhana argues the next step is making data intelligent while it moves and retrieving context across distributed environments only when it is needed. That expands the company's addressable market from SIEM economics to the broader data foundation supporting enterprise applications and AI agents.

The expansion is logical, but it also raises the execution bar. A security-data product can succeed by reducing noise and cost within a defined workflow. A control plane must earn trust across teams, systems, governance models, and cloud environments. DataBahn now has to prove its neutral orchestration layer remains technically valuable as its buyers and use cases extend well beyond the security organization.

What the Funding Signals

The Series B reflects a broader shift from accumulating data to governing access, context, and cost. Enterprises already operate warehouses, data lakes, SIEMs, observability platforms, and an expanding collection of AI services. The scarce capability is increasingly becoming the connective layer that improves information in transit, applies policy, and allows each system to consume only the data it actually needs.

DataBahn has raised $40M to make that layer a category rather than a feature buried inside another platform. If the company can translate its reported retention and proof-of-concept performance into broader enterprise deployments, this round could mark its transition from a security-data specialist to an enterprise AI infrastructure provider. The capital gives DataBahn more capacity for R&D and market expansion. The real test is whether customers come to view its agentic data control plane as essential infrastructure rather than another stop in an already crowded enterprise data stack.