DataBahn.ai raised $17 million in Series A funding.
The round was led by Forgepoint Capital, with participation from S3 Ventures and returning investor GTM Capital, bringing the company's total capital raised to $19 million.
The funding will accelerate the development of the DataBahn platform roadmap for agentic AI — autonomous agents that learn from enterprise data flows to automate data engineering tasks—and support global expansion as the company establishes itself as the trusted foundation for enterprises seeking clarity, control and composability in their data pipelines.
DataBahn.ai is setting a new benchmark for how modern enterprises manage and operationalize telemetry across security, observability, IOT/OT and AI ecosystems. The DataBahn platform delivers a dynamic, AI-native data fabric that allows organizations to seamlessly integrate, govern and optimize data pipelines from any source to any destination — with one-click simplicity and enterprise-grade control.
DataBahn's new Phantom agents collect telemetry without deploying traditional agents, avoiding footprint bloat and preserving compute resources. Built on a revolutionary AI-driven architecture, DataBahn parses, enriches and suppresses noise at scale, all while also being mindful of egress costs. The platform's new federated search capabilities deliver persona-based insights; it's beyond just using SQL queries. For security teams, this means faster threat detection and streamlined compliance. For observability teams, better predictive analytics for IT outage prevention. For business teams, deeper application transaction visibility. For the enterprise as a whole, DataBahn unlocks the full value of data — without compromise.
"Today's enterprises don't just need data pipelines; they need intelligent fabrics that adapt, govern and optimize data at scale," said Nanda Santhana, co-founder and CEO of DataBahn.ai. "We're building the foundation for a new era of observability, one where data is not just moved, but understood, enriched and made AI-ready in real time."
"Enterprises aren't just overwhelmed by data volume; they're being outpaced by its complexity," said Santhana. "Our mission is to transform telemetry from a liability into a strategic asset by making data pipelines smarter, leaner and AI-ready from the start."
As part of the Series A round, Ernie Bio, managing director at Forgepoint Capital, has joined the DataBahn.ai board of directors. "DataBahn is tackling one of the most urgent infrastructure challenges: how to manage and extract value from fragmented, fast-growing data streams," said Bio. "What's truly rare is the customer enthusiasm. We heard consistent praise for the platform's rapid ROI, forward-looking innovation and the team's responsiveness—qualities that separate great companies from the rest."
Originally designed to address the unique challenges of cybersecurity, IoT and OT telemetry, the DataBahn platform has rapidly evolved into a unified control plane for enterprise data. Its expansion into application, infrastructure and observability workloads reflects a growing demand for intelligent, end-to-end visibility across the modern data lifecycle.
"We didn't set out to build just another pipeline. We built DataBahn to make data work for security and IT teams—not the other way around" said Nithya Nareshkumar, co-founder and president of DataBahn.ai. "By combining deep domain knowledge with plug-and-play AI, we're helping teams break through complexity and unlock insight from day one—no rewiring, no retraining."
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.