Goutham (Gou) Rao is the CEO and co-founder of NeuBird, the creators of Hawkeye, the world’s first generative AI-powered ITOps engineer, designed to assist IT groups diagnose and resolve technical points immediately, enabling seamless collaboration between human groups and AI.
A serial entrepreneur with a confirmed monitor file, Rao has co-founded and efficiently exited a number of firms. He co-founded Portworx, acquired by Pure Storage; Ocarina Networks, acquired by Dell; and Net6, acquired by Citrix. He’s additionally an achieved inventor with over 50 issued patents spanning laptop networking, storage, and safety.
NeuBird is growing generative AI options for IT operations to assist handle the scarcity of expert professionals wanted to handle trendy, complicated know-how stacks. The corporate focuses on simplifying knowledge evaluation and offering real-time actionable insights, aiming to reinforce effectivity and assist innovation in IT administration.
What impressed you to launch NeuBird, and the way did you establish the necessity for AI-driven IT operations automation?
NeuBird was born out of the rising complexity of enterprise IT stacks and the scarcity of expert IT professionals. Conventional instruments weren’t maintaining, forcing IT groups to spend 30% of their budgets navigating siloed knowledge sources as an alternative of driving innovation. We noticed a chance to create an AI-powered ITOps engineer—Hawkeye—that might immediately pinpoint IT points, cut back time-to-resolution from days to minutes, and allow enterprises to scale IT operations with out being bottlenecked by labor constraints.
How is NeuBird pioneering AI-powered digital teammates, and what units Hawkeye aside from conventional IT automation instruments?
In contrast to static, rule-based IT automation instruments, our AI-powered digital teammate, Hawkeye, dynamically processes huge telemetry knowledge and diagnoses points immediately. It eliminates the bias of pre-programmed observability instruments by pulling insights from numerous enterprise knowledge sources—together with Slack, cloud providers, databases, and customized purposes—giving IT groups a holistic, contextualized view of their infrastructure.
Hawkeye doesn’t simply floor alerts; it actively collaborates with engineers by way of a conversational interface, diagnosing root causes and proposing fixes to complicated IT points. This essentially adjustments how IT operations work, serving to them decrease downtime and reply to IT incidents with unprecedented pace.
Enterprises usually wrestle with knowledge overload in IT operations. How does Hawkeye filter by way of huge knowledge units to supply actionable insights?
Conventional IT instruments wrestle to course of the flood of telemetry knowledge—logs, system metrics, and cloud efficiency indicators—resulting in alert fatigue and sluggish incident decision.
Hawkeye cuts by way of the noise by repeatedly analyzing real-time knowledge, and detecting patterns that sign efficiency points or failures. It enhances current observability and monitoring instruments by going past passive monitoring to take proactive motion. Performing as an engineer in your crew, it interprets IT telemetry and system knowledge out of your present instruments, diving into points and resolving them as they come up.
It delivers clear, actionable insights in pure language, decreasing response instances from days to minutes.
Hawkeye’s distinctive strategy leverages the facility of LLMs to information incident evaluation with out ever sharing buyer knowledge with LLMs, making certain a considerate and safe strategy.
Safety and belief are main issues for AI adoption in IT. How is NeuBird addressing these challenges?
Hawkeye’s distinctive strategy leverages the facility of LLMs to information incident evaluation with out ever sharing buyer knowledge with LLMs, making certain a considerate and safe strategy.
Hawkeye operates inside an enterprise’s safety perimeter, utilizing solely inside knowledge sources to generate insights—eliminating hallucinations that plague generic LLM-based methods. It additionally ensures transparency by offering traceable suggestions, so IT groups keep full management over decision-making. This strategy makes it a dependable and safe AI teammate quite than a black-box resolution.
How does Hawkeye combine with current IT infrastructure, and what does the onboarding course of appear like for enterprises?
Hawkeye seamlessly integrates with enterprise IT environments by connecting to current observability, monitoring and incident response instruments, e.g. AWS CloudWatch, Azure Monitor, Datadog, and PagerDuty. It really works alongside IT, DevOps, and SRE groups with out requiring main infrastructure adjustments.
Right here’s the way it works:
- Deployment: Hawkeye is deployed inside your atmosphere, connecting to current instruments and knowledge sources.
- Studying & Adaptation: It analyzes historic incidents and real-time telemetry to grasp regular system operations and establish patterns.
- Customization: The platform adapts to enterprise-specific workflows, tailoring responses and suggestions to operational wants.
- Collaboration: Via a chat-based interface, groups obtain real-time diagnostics, options, and automatic resolutions the place relevant.
This streamlined integration course of accelerates incident decision, reduces MTTR, and enhances system reliability—permitting enterprises to scale IT operations effectively with out including headcount.
What function do human engineers play alongside AI teammates like Hawkeye? How do you see this collaboration evolving?
Hawkeye dietary supplements, quite than replaces, human IT professionals. IT groups nonetheless drive strategic selections, however as an alternative of manually troubleshooting each concern, they work alongside Hawkeye to diagnose and resolve issues sooner. As AI teammates turn into extra superior, IT professionals will shift towards higher-value duties—optimizing architectures, enhancing safety, and accelerating new know-how adoption.
Hawkeye claims to cut back imply time to decision (MTTR) by 90%. Are you able to share any real-world examples or case research that reveal this influence?
A nationwide grocery retailer built-in Hawkeye to deal with the rising complexity of its e-commerce platform. Their SRE crew was overwhelmed by huge telemetry knowledge and sluggish handbook investigations, particularly throughout peak purchasing durations.
With Hawkeye as a GenAI-powered teammate, they noticed:
- ~90% MTTR discount – Prompt knowledge correlation throughout AWS CloudWatch, AWS MSK, and PagerDuty.
- 24/7 real-time evaluation – Eradicated after-hours escalations.
- Automated incident decision – Pre-approved fixes deployed autonomously.
Throughout their vacation purchasing surge, Hawkeye optimized capability, detected early points, and made real-time scaling changes, making certain close to 100% uptime—a game-changer for his or her IT operations.
What’s your imaginative and prescient for the evolution of AI brokers from passive assistants to energetic problem-solvers in enterprise operations, and what key developments are driving this shift?
AI is shifting from passive observability to energetic problem-solving. Hawkeye already supplies root-cause evaluation and resolutions, however the subsequent part is full autonomy—the place AI proactively optimizes IT operations, and self-heals infrastructure in actual time. This evolution, pushed by developments in GenAI and cognitive decision-making fashions, will redefine enterprise IT.
The place do you see AI-driven enterprise automation within the subsequent 5 years, and what main challenges or breakthroughs do you anticipate alongside the way in which?
AI will shift from aiding engineers to completely autonomous IT operations, predicting and resolving points earlier than they escalate. Multi-agent AI workflows will allow seamless collaboration throughout IT, safety, and DevOps, breaking down silos between departments. The most important breakthroughs will embody self-healing infrastructure, AI-driven cross-functional collaboration, and stronger human-AI belief, permitting AI teammates to tackle extra complicated selections. The primary challenges will probably be making certain AI transparency and adapting the workforce to work alongside AI, balancing automation with human oversight.
Having led a number of startups to success, what recommendation would you give to entrepreneurs constructing AI-driven firms in the present day?
Entrepreneurs ought to give attention to fixing actual, high-value issues quite than chasing AI hype. AI should be constructed with enterprise belief in thoughts, making certain transparency and management for companies adopting it. Adaptability is essential—AI methods should evolve with enterprise wants as an alternative of being inflexible, one-size-fits-all options. Relatively than changing human experience, AI needs to be positioned as a teammate that enhances decision-making and operational effectivity. Lastly, enterprise AI adoption takes time, so firms that prioritize scalability and long-term influence over short-term tendencies will in the end emerge as leaders within the area.
Thanks for the good interview, readers who want to be taught extra ought to go to NeuBird.