Today we're announcing $3 million in Seed funding led by Kalaari Capital, with participation from prominent technology founders. The capital will accelerate product development, expand our enterprise go-to-market, and scale customer deployment as we grow across global cloud environments.
NudgeBee is a multi-functional AI-agentic platform for Cloud Operations, already in production with enterprise customers across healthcare, financial services, logistics, technology, and managed cloud services, including Rackspace.
NudgeBee was founded in 2024 from a simple observation. While building data platforms for Global 2000 enterprises, our team watched Day-2 cloud operations grow into one of the hardest problems in modern engineering, and watched the tooling fall further behind every quarter. This funding lets us go deeper on the platform built to close that gap.
Why we built NudgeBee
We built NudgeBee to close the gap between observability and execution in enterprise cloud operations.
Before NudgeBee, our co-founders Rakesh Rajendran and Shiv Pratap Singh spent years at Saama, a Bay Area data and analytics company, building enterprise data platforms for Global 2000 customers. Rakesh led Delivery and Operations. Shiv led data platform engineering.
Across customer after customer, they kept seeing the same thing. The dashboards lit up. The alerts fired. The data was there. And the actual work of resolving incidents, trimming cloud waste, and keeping Kubernetes healthy still came down to a senior engineer and a Slack channel at 2 a.m.
Observability had matured. Execution hadn't. That's the gap NudgeBee is built to close.
"Teams have no shortage of dashboards. What they lack is connected context and reliable execution. NudgeBee shifts this model from observation to execution, where AI agents do not just identify issues but act on them within existing workflows, making operations faster, more reliable, and far less manual."
Rakesh Rajendran, Co-founder & CEO, NudgeBee
What's broken in Day-2 Cloud Ops
Day-2 cloud operations is broken because enterprises have visibility but lack a reliable execution layer. Most large organizations now have solid visibility into their multicloud and Kubernetes environments. What they don't have is a governed system that connects detection to action.
The pattern is the same across most teams we talk to. MTTR keeps climbing because troubleshooting needs context from observability, ticketing, and deployment systems that don't talk to each other. Cost-saving recommendations sit on a dashboard but never get implemented. Kubernetes upgrades get deferred because no one has the bandwidth. L1 and L2 teams escalate too often because they don't have production context. And enterprise AI adoption stalls at the procurement gate because most agentic tools can't satisfy RBAC, approvals, audit, and deployment requirements.
The common factor isn't a shortage of data. It's the absence of a system that can act on that data safely, inside the workflows enterprises already run.
What NudgeBee does differently
NudgeBee differs from legacy AIOps and observability platforms in three ways: a live Semantic Knowledge Graph and RAG, zero data ingestion, and a unified execution layer that powers both pre-built and custom agents.
A live Semantic Knowledge Graph. Most AI-for-ops tools use retrieval-augmented generation over ingested telemetry. That works for chat. It breaks down when the AI needs to reason about a topology change, a service dependency, or a workflow that's halfway through executing. NudgeBee uses a live Semantic Knowledge Graph that models infrastructure topology, service dependencies, workloads, and workflow state in real time. Agents reason from the actual structure of your environment.
Zero data ingestion. NudgeBee queries your existing observability, ticketing, cloud, and Kubernetes systems in place. No pipeline to build, no egress cost, no centralization of sensitive operational data into yet another vendor's lake. This removes the most common enterprise blocker to AI in operations: "we can't move our data."
A unified execution layer. NudgeBee ships with pre-built AI agents for SRE troubleshooting, FinOps optimization, Kubernetes operations, and support workflows. For everything unique to your environment, the AIOps Agentic Automation Builder lets your team encode repetitive operational work into custom workflows, without dealing with model plumbing, orchestration glue, or eval overhead. Both draw from the same Knowledge Graph, so NudgeBee can correlate signals across systems and act inside your real engineering workflows: pull requests, tickets, approval-gated automations, Slack and Teams.
All of this runs inside enterprise guardrails by design. Role-based access controls, approval workflows, immutable audit logs, deterministic execution modes alongside agentic ones, self-hosted and on-premise deployment options, and bring-your-own-model flexibility.
"Enterprise operations are fundamentally a context problem. Knowledge gets fragmented across people, scripts, dashboards, and tools, and that fragmentation is what slows teams down. Once you unify telemetry, topology, history, and workflow state, AI agents can operate with far greater precision and safety. That is what lets us go from insight to action in a way legacy tooling simply cannot."
Shiv Pratap Singh, Co-founder & CTO, NudgeBee
What customers are seeing
NudgeBee customers consistently see up to 50% MTTR reduction, 40 to 60% reduction in wasted cloud spend, 30 to 40% productivity gains in operations activity, a 6x increase in ticket analysis throughput, and 5x faster operations automation.
We're already in production across healthcare, financial services, logistics, technology, and managed cloud services. A few outcomes from early deployments:
- A NASDAQ-listed technology firm reduced MTTR from hours to minutes and cut Level 3 escalations by 68%.
- A large healthcare enterprise achieved $1.2M in annual cloud cost savings and a 40% reduction in wasted Kubernetes resources, savings the IT team is using to fund new digital health initiatives instead of new headcount.
- A global logistics provider automated certificate renewals, vulnerability scans, and routine Kubernetes operations, reducing Kubernetes-related support tickets by 70%.
- A global enterprise support organization saw a 50% improvement in ticket-handling productivity, with a higher share of issues resolved at L1.
Rackspace, one of the world's largest managed cloud providers, is among our enterprise customers.
"Multicloud complexity does not slow down when your team does. NudgeBee's SRE and AIOps agents absorb routine operations autonomously so our teams focus on high-value engineering. Their extensive library of specialist agents gives Rackspace the building blocks to rapidly create custom agentic workflows at scale."
Nirmal Ranganathan, CTO, Rackspace
See what NudgeBee can do on your stack
Why Kalaari, and what's next
Kalaari Capital led this round because of shared conviction in the AI-for-operations category and a long track record of backing enterprise software and deep-tech companies building for global markets. The operational support and network the firm brings to portfolio companies made it a deliberate choice to lead.
"At Kalaari, we believe the next phase of infrastructure tooling will be defined by systems that not only identify problems but resolve them. NudgeBee stands out in its ability to connect signals across the stack and move teams from diagnosis to action within real engineering workflows. With strong early traction, Rakesh and Shiv have validated both the depth of the problem and the strength of their approach, and we're excited to partner with them on this journey."
Sampath P, Partner, Kalaari Capital
The funds go into three priorities.
Product engineering. Continued investment in the platform, the Semantic Knowledge Graph, and the Enterprise Context Layer, with faster iteration on pre-built agents, deeper integrations, and expanded AIOps Automation Builder capabilities.
Go-to-market. A focused partnership and channel-led motion alongside direct enterprise sales. In enterprise cloud, trust and implementation depth compound faster than marketing spend, so we're investing in the consulting, cloud, and services partners that enterprise customers already work with.
Customer success. Expanded deployment and success capacity to shorten time-to-value, which is the strongest predictor of sustained enterprise adoption.
How to engage with us
If you're an SRE, Platform, CloudOps, or FinOps leader evaluating how AI-agentic workflows can reduce MTTR and automate Day-2 operations, the best next step is a 20-minute conversation with our team. Book a demo
If you're a consulting, cloud, or services partner building agentic ops practices for your customers, we'd love to talk. Partner with us
FAQs
Who funded NudgeBee?
NudgeBee raised $3 million in Seed funding led by Kalaari Capital, with participation from prominent technology founders. The round was announced in April 2026.
What does NudgeBee do?
NudgeBee is a multi-functional AI-agentic platform for Cloud Operations. It combines pre-built AI agents for SRE troubleshooting, FinOps optimization, Kubernetes operations, and support workflows with an AIOps Agentic Automation Builder for custom workflows. The platform helps enterprises reduce MTTR, cut wasted cloud spend, and automate Day-2 operations safely, with full RBAC, approvals, and audit trails.
Who founded NudgeBee?
NudgeBee was founded in 2024 by Rakesh Rajendran (Co-founder & CEO) and Shiv Pratap Singh (Co-founder & CTO). Both previously worked at Saama, a Bay Area data and analytics company, where Rakesh led Delivery and Operations and Shiv led data platform engineering. Rakesh also holds patents in distributed systems.
How much has NudgeBee raised?
NudgeBee has raised $3 million in Seed funding, led by Kalaari Capital, with participation from prominent technology founders.
Where is NudgeBee headquartered?
NudgeBee is headquartered in Pune, India, with customers across the United States, India, and global enterprise cloud environments.
How is NudgeBee different from traditional AIOps tools?
NudgeBee differs from traditional AIOps platforms in three ways. First, it uses a live Semantic Knowledge Graph that models infrastructure topology and workflow state in real time, instead of relying on retrieval-augmented generation (RAG) over ingested telemetry. Second, it queries existing observability, ticketing, and cloud systems in place with zero data ingestion, eliminating egress costs and data centralization risk. Third, it provides a unified execution layer that combines pre-built AI agents with a custom workflow builder, allowing teams to act on signals inside their existing engineering workflows rather than just generating alerts.
What is an AI-agentic platform for cloud operations?
An AI-agentic platform for cloud operations uses autonomous AI agents to handle Day-2 operational tasks such as incident troubleshooting, cloud cost optimization, Kubernetes maintenance, and ticket triage. Unlike traditional observability tools that surface problems on dashboards, agentic platforms close the loop by acting on those signals within enterprise workflows, with appropriate guardrails like role-based access control, approval gates, and audit logging.
Is NudgeBee available for self-hosted or on-premise deployment?
Yes. NudgeBee supports self-hosted, on-premise, and SaaS deployments, with bring-your-own-model flexibility for enterprises that need to run on their own infrastructure or use their preferred LLM providers.