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 help IT teams diagnose and resolve technical issues instantly, enabling seamless collaboration between human teams and AI.
A serial entrepreneur with a proven track record, Rao has co-founded and successfully exited multiple companies. He co-founded Portworx, acquired by Pure Storage; Ocarina Networks, acquired by Dell; and Net6, acquired by Citrix. He is also an accomplished inventor with over 50 issued patents spanning computer networking, storage, and security.
NeuBird is developing generative AI solutions for IT operations to help address the shortage of skilled professionals needed to manage modern, complex technology stacks. The company focuses on simplifying data analysis and providing real-time actionable insights, aiming to enhance efficiency and support innovation in IT management.
What inspired you to launch NeuBird, and how did you identify the need for AI-driven IT operations automation?
NeuBird was born out of the growing complexity of enterprise IT stacks and the shortage of skilled IT professionals. Traditional tools weren’t keeping up, forcing IT teams to spend 30% of their budgets navigating siloed data sources instead of driving innovation. We saw an opportunity to create an AI-powered ITOps engineer—Hawkeye—that could instantly pinpoint IT issues, reduce time-to-resolution from days to minutes, and enable enterprises to scale IT operations without being bottlenecked by labor constraints.
How is NeuBird pioneering AI-powered digital teammates, and what sets Hawkeye apart from traditional IT automation tools?
Unlike static, rule-based IT automation tools, our AI-powered digital teammate, Hawkeye, dynamically processes vast telemetry data and diagnoses issues instantly. It eliminates the bias of pre-programmed observability tools by pulling insights from diverse enterprise data sources—including Slack, cloud services, databases, and custom applications—giving IT teams a holistic, contextualized view of their infrastructure.
Hawkeye doesn’t just surface alerts; it actively collaborates with engineers through a conversational interface, diagnosing root causes and proposing fixes to complex IT issues. This fundamentally changes how IT operations work, helping them minimize downtime and respond to IT incidents with unprecedented speed.
Enterprises often struggle with data overload in IT operations. How does Hawkeye filter through massive data sets to provide actionable insights?
Traditional IT tools struggle to process the flood of telemetry data—logs, system metrics, and cloud performance indicators—leading to alert fatigue and slow incident resolution.
Hawkeye cuts through the noise by continuously analyzing real-time data, and detecting patterns that signal performance issues or failures. It complements existing observability and monitoring tools by going beyond passive monitoring to take proactive action. Acting as an engineer on your team, it interprets IT telemetry and system data from your current tools, diving into issues and resolving them as they arise.
It delivers clear, actionable insights in natural language, reducing response times from days to minutes.
Hawkeye’s unique approach leverages the power of LLMs to guide incident analysis without ever sharing customer data with LLMs, ensuring a thoughtful and secure approach.
Security and trust are major concerns for AI adoption in IT. How is NeuBird addressing these challenges?
Hawkeye’s unique approach leverages the power of LLMs to guide incident analysis without ever sharing customer data with LLMs, ensuring a thoughtful and secure approach.
Hawkeye operates within an enterprise’s security perimeter, using only internal data sources to generate insights—eliminating hallucinations that plague generic LLM-based systems. It also ensures transparency by providing traceable recommendations, so IT teams maintain full control over decision-making. This approach makes it a reliable and secure AI teammate rather than a black-box solution.
How does Hawkeye integrate with existing IT infrastructure, and what does the onboarding process look like for enterprises?
Hawkeye seamlessly integrates with enterprise IT environments by connecting to existing observability, monitoring and incident response tools, e.g. AWS CloudWatch, Azure Monitor, Datadog, and PagerDuty. It works alongside IT, DevOps, and SRE teams without requiring major infrastructure changes.
Here’s how it works:
- Deployment: Hawkeye is deployed within your environment, connecting to existing tools and data sources.
- Learning & Adaptation: It analyzes historical incidents and real-time telemetry to understand normal system operations and identify patterns.
- Customization: The platform adapts to enterprise-specific workflows, tailoring responses and recommendations to operational needs.
- Collaboration: Through a chat-based interface, teams receive real-time diagnostics, solutions, and automated resolutions where applicable.
This streamlined integration process accelerates incident resolution, reduces MTTR, and enhances system reliability—allowing enterprises to scale IT operations efficiently without adding headcount.
What role do human engineers play alongside AI teammates like Hawkeye? How do you see this collaboration evolving?
Hawkeye supplements, rather than replaces, human IT professionals. IT teams still drive strategic decisions, but instead of manually troubleshooting every issue, they work alongside Hawkeye to diagnose and resolve problems faster. As AI teammates become more advanced, IT professionals will shift toward higher-value tasks—optimizing architectures, improving security, and accelerating new technology adoption.
Hawkeye claims to reduce mean time to resolution (MTTR) by 90%. Can you share any real-world examples or case studies that demonstrate this impact?
A national grocery retailer integrated Hawkeye to handle the growing complexity of its e-commerce platform. Their SRE team was overwhelmed by massive telemetry data and slow manual investigations, especially during peak shopping periods.
With Hawkeye as a GenAI-powered teammate, they saw:
- ~90% MTTR reduction – Instant data correlation across AWS CloudWatch, AWS MSK, and PagerDuty.
- 24/7 real-time analysis – Eliminated after-hours escalations.
- Automated incident resolution – Pre-approved fixes deployed autonomously.
During their holiday shopping surge, Hawkeye optimized capacity, detected early issues, and made real-time scaling adjustments, ensuring near 100% uptime—a game-changer for their IT operations.
What is your vision for the evolution of AI agents from passive assistants to active problem-solvers in enterprise operations, and what key advancements are driving this shift?
AI is shifting from passive observability to active problem-solving. Hawkeye already provides root-cause analysis and resolutions, but the next phase is full autonomy—where AI proactively optimizes IT operations, and self-heals infrastructure in real time. This evolution, driven by advancements in GenAI and cognitive decision-making models, will redefine enterprise IT.
Where do you see AI-driven enterprise automation in the next five years, and what major challenges or breakthroughs do you anticipate along the way?
AI will shift from assisting engineers to fully autonomous IT operations, predicting and resolving issues before they escalate. Multi-agent AI workflows will enable seamless collaboration across IT, security, and DevOps, breaking down silos between departments. The biggest breakthroughs will include self-healing infrastructure, AI-driven cross-functional collaboration, and stronger human-AI trust, allowing AI teammates to take on more complex decisions. The main challenges will be ensuring AI transparency and adapting the workforce to work alongside AI, balancing automation with human oversight.
Having led multiple startups to success, what advice would you give to entrepreneurs building AI-driven companies today?
Entrepreneurs should focus on solving real, high-value problems rather than chasing AI hype. AI must be built with enterprise trust in mind, ensuring transparency and control for businesses adopting it. Adaptability is key—AI systems must evolve with business needs instead of being rigid, one-size-fits-all solutions. Rather than replacing human expertise, AI should be positioned as a teammate that enhances decision-making and operational efficiency. Finally, enterprise AI adoption takes time, so companies that prioritize scalability and long-term impact over short-term trends will ultimately emerge as leaders in the space.
Thank you for the great interview, readers who wish to learn more should visit NeuBird.
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