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DataBahn.ai Raises $17M Series A Funding

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."

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Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

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For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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DataBahn.ai Raises $17M Series A Funding

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 MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...