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 teams diagnose and resolve technical issues immediately, enabling seamless collaboration between human teams and AI.
A serial entrepreneur with a proven track record, Rao has co-founded and successfully exited multiple corporations. He co-founded Portworx, acquired by Pure Storage; Ocarina Networks, acquired by Dell; and Net6, acquired by Citrix. He can be an achieved inventor with over 50 issued patents spanning computer networking, storage, and security.
NeuBird is developing generative AI solutions for IT operations to assist address the shortage of expert professionals needed to administer modern, complex technology stacks. The corporate focuses on simplifying data evaluation and providing real-time actionable insights, aiming to boost efficiency and support innovation in IT management.
What inspired you to launch NeuBird, and the way did you discover the necessity for AI-driven IT operations automation?
NeuBird was born out of the growing complexity of enterprise IT stacks and the shortage of expert IT professionals. Traditional tools weren’t maintaining, forcing IT teams to spend 30% of their budgets navigating siloed data sources as an alternative of driving innovation. We saw a possibility to create an AI-powered ITOps engineer—Hawkeye—that would immediately 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 other than 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 immediately. 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 reply to IT incidents with unprecedented speed.
Enterprises often struggle with data overload in IT operations. How does Hawkeye filter through massive data sets to supply actionable insights?
Traditional IT tools struggle to process the flood of telemetry data—logs, system metrics, and cloud performance indicators—resulting in alert fatigue and slow incident resolution.
Hawkeye cuts through the noise by repeatedly 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 motion. Acting as an engineer in your team, it interprets IT telemetry and system data out of 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 facility of LLMs to guide incident evaluation 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 facility of LLMs to guide incident evaluation without ever sharing customer data with LLMs, ensuring a thoughtful and secure approach.
Hawkeye operates inside 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 moderately than a black-box solution.
How does Hawkeye integrate with existing IT infrastructure, and what does the onboarding process appear 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 really works alongside IT, DevOps, and SRE teams without requiring major infrastructure changes.
Here’s how it really works:
- Deployment: Hawkeye is deployed inside your environment, connecting to existing tools and data sources.
- Learning & Adaptation: It analyzes historical incidents and real-time telemetry to grasp normal system operations and discover patterns.
- Customization: The platform adapts to enterprise-specific workflows, tailoring responses and proposals to operational needs.
- Collaboration: Through a chat-based interface, teams receive real-time diagnostics, solutions, and automatic 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, moderately than replaces, human IT professionals. IT teams still drive strategic decisions, but as an alternative of manually troubleshooting every issue, they work alongside Hawkeye to diagnose and resolve problems faster. As AI teammates turn into more advanced, IT professionals will shift toward higher-value tasks—optimizing architectures, improving security, and accelerating latest technology adoption.
Hawkeye claims to scale back mean time to resolution (MTTR) by 90%. Are you able to share any real-world examples or case studies that reveal 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 – Fast data correlation across AWS CloudWatch, AWS MSK, and PagerDuty.
- 24/7 real-time evaluation – Eliminated after-hours escalations.
- Automated incident resolution – Pre-approved fixes deployed autonomously.
During their holiday shopping surge, Hawkeye optimized capability, detected early issues, and made real-time scaling adjustments, ensuring near 100% uptime—a game-changer for his or her IT operations.
What’s your vision for the evolution of AI agents from passive assistants to lively problem-solvers in enterprise operations, and what key advancements are driving this shift?
AI is shifting from passive observability to lively problem-solving. Hawkeye already provides root-cause evaluation and resolutions, but the following 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 following five years, and what major challenges or breakthroughs do you anticipate along the best way?
AI will shift from assisting engineers to completely 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 largest breakthroughs will include self-healing infrastructure, AI-driven cross-functional collaboration, and stronger human-AI trust, allowing AI teammates to tackle more complex decisions. The principal challenges might 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 constructing AI-driven corporations today?
Entrepreneurs should give attention to solving real, high-value problems moderately than chasing AI hype. AI have to be built with enterprise trust in mind, ensuring transparency and control for businesses adopting it. Adaptability is vital—AI systems must evolve with business needs as an alternative of being rigid, one-size-fits-all solutions. Somewhat than replacing human expertise, AI must be positioned as a teammate that enhances decision-making and operational efficiency. Finally, enterprise AI adoption takes time, so corporations that prioritize scalability and long-term impact over short-term trends will ultimately emerge as leaders within the space.
