Goutham (Gou) Rao is CEO and Co-founder neubirdThe creator of Hawkeye is the world’s first vivid AI-driven ITOPS engineer, aiming to help IT teams diagnose and solve technical problems immediately, thus enabling seamless collaboration between human teams and AI.
Rao is a serial entrepreneur with a strong track record and has co-founded and successfully exited several companies. He co-founded Portworx, acquired for pure storage; Ceramic Network, acquired by Dell; and Net6, acquired by Citrix. He is also an outstanding inventor with over 50 issued patents covering computer networking, storage and security.
Neubird is developing generated AI solutions for IT operations to help address the shortage of skilled professionals needed to manage modern, complex technology stacks. The company is committed to simplifying data analytics and delivering real-time actionable insights aimed at increasing efficiency and supporting innovation in IT management.
What prompted you to launch Neubird, and how do you determine the need for AI-driven IT operations automation?
Neubird was born out of the growing complexity of IT enterprises and the shortage of skilled IT professionals. Traditional tools are not keeping up, forcing IT teams to spend 30% of their budget to navigate siloed data sources rather than drive innovation. We see the opportunity to create an AI-powered ITOPS engineer Hawkeye who can immediately pinpoint its problems, reduce time to resolution from days to days, and enable businesses to scale IT operations without labor-restricted bottles.
Neubird pioneered AI-powered digital teammates, what distinguishes Hawkeye from traditional IT automation tools?
Unlike static, rules-based IT automation tools, our AI-driven digital teammate Hawkeye dynamically processes large amounts of telemetry data and diagnoses problems immediately. It removes bias from pre-programmed observability tools by extracting insights from a variety of enterprise data sources, including Slack, cloud services, databases, and custom applications, allowing IT teams to give a holistic, contextualized view of their infrastructure.
Hawkeye is not only a surface alert; it actively works with engineers through a dialogue interface to diagnose root causes and propose fixes for complex IT issues. This fundamentally changes how it operates, helping them minimize downtime and respond to IT events at an unprecedented pace.
Enterprises often struggle with data overload in IT operations. How does Hawkeye filter through large data sets to provide actionable insights?
Traditional IT tools work hard to handle floods of telemetry data (quantity, system metrics, and cloud performance metrics) to improve fatigue and slow event solutions.
Hawkeye reduces noise by continuously analyzing real-time data and detecting patterns of signal performance emitted or failed. It complements existing observability and surveillance tools by moving beyond passive monitoring to take initiatives. It is the engineer on your team that explains IT telemetry and system data from your current tools, so that you can sneak into problems and solve them when they arise.
It provides clear, actionable insights in natural language, reducing response times from days to minutes.
Hawkeye’s unique approach uses the power of LLM to guide event analysis without sharing customer data with LLMS, ensuring a thoughtful and secure approach.
Security and trust are the main issues in AI adoption. How does Neubird deal with these challenges?
Hawkeye’s unique approach uses the power of LLM to guide event analysis without sharing customer data with LLMS, ensuring a thoughtful and secure approach.
Hawkeye operates around the security of an enterprise and uses only internal data sources to generate insights, which are hallucinations that plague general-purpose systems based on LLM. It also ensures transparency by providing traceable advice so IT teams maintain full control over decision making. This approach makes it a reliable and secure AI teammate, not a black box solution.
How does Hawkeye integrate with existing IT infrastructure, and what is the onboarding process for an enterprise?
Hawkeye seamlessly integrates with enterprise IT environments by connecting to existing observability, monitoring and incident response tools such as AWS CloudWatch, Azure Monitor, DataDog, and Pagerduty. It works with it, and DevOps and SRE teams do not require significant infrastructure.
Here is how it works:
- deploy: Hawkeye is deployed in your environment, connecting to existing tools and data sources.
- Learning and adapting: It analyzes historical events and real-time telemetry to understand normal system operations and identify patterns.
- Custom: The platform adapts to enterprise-specific workflows, adjusts responses and suggestions for operational needs.
- cooperate: With a chat-based interface, teams will get real-time diagnostics, solutions and automated solutions where applicable.
This simplified integration process accelerates event resolution, reduces MTTR and enhances system reliability, allowing enterprises to effectively scale IT operations without adding employees.
What role does human engineers play with AI teammates like Hawkeye? How do you view the development of this kind of cooperation?
Hawkeye supplements instead of substituting human IT professionals. Its team is still driving strategic decisions, but instead of manually troubleshooting each issue, they work with Hawkeye to diagnose and resolve issues faster. As AI teammates become more advanced, IT professionals will move towards high-value tasks to advanced tasks – bringing architectures to a height, increasing security and accelerating the adoption of new technologies.
Hawkeye claims to reduce mean resolution (MTTR) by 90%. Can you share any real-world examples or case studies that demonstrate this impact?
A national grocery retailer has fused Hawkeye to deal with the growing complexity of its e-commerce platform. Their SRE team was flooded with massive telemetry data and slow manual surveys, especially during peak shopping.
Their teammates driven by Hawkeye as Genai saw:
- ~90% MTTR restore – Instant data correlation between AWS CloudWatch, AWS MSK and PAGERDUTY.
- 24/7 real-time analysis – Eliminate after get off work upgrades.
- Automatic event solution – Pre-approved automatic deployment of repairs.
In the holiday shopping wave, Hawkeye optimized capacity, detecting early issues and making real-time scaling adjustments, ensuring close to 100% uptime, a game-changer for its IT operations.
What do you think about the development of AI agents from passive assistants to people who actively solve problems in business operations?
Artificial intelligence shifts from passive observability to proactive problem solving. Hawkeye has provided root cause analysis and solutions, but the next stage is completely autonomous, with AI proactively optimizing IT operations and building its infrastructure itself in real time. This evolution is driven by advances in Genai and cognitive decision-making models that will redefine businesses.
Where can you see AI-driven enterprise automation over the next five years and what major challenges or breakthroughs do you expect to bring along the way?
AI will move from assisting engineers to fully autonomous IT operations, predicting and solving problems before upgrading. Multi-agent AI workflows will decompose silos between the departments. The biggest breakthroughs will include self-healing infrastructure, AI-driven cross-functional collaboration, and stronger human trust, enabling AI teammates to make more complex decisions. The main challenge will be to ensure AI transparency and adapt to the workforce that works with AI, thereby balancing automation with human supervision.
After leading multiple startups to success, what advice would you give to today’s entrepreneurs who build AI-driven companies?
Entrepreneurs should focus on solving real high-value problems rather than pursuing AI hype. AI must keep corporate trust in mind to ensure transparency and control of the businesses that adopt it. Adaptability is key – AI systems must evolve with business needs, not rigid and all solutions to a certain degree. Instead of replacing human expertise, AI should be positioned as a teammate who improves decision-making and operational efficiency. Finally, the adoption of enterprise AI takes time, so companies that prioritize scalability and long-term impact will eventually become leaders in the field.
Thanks for your excellent interview, and readers who hope to learn more should visit Neubird.
