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 corporations. He co-founded Portworx, acquired by Pure Storage; Ocarina Networks, acquired by Dell; and Net6, acquired by Citrix. He’s additionally an completed inventor with over 50 issued patents spanning pc networking, storage, and safety.
NeuBird is growing generative AI options for IT operations to assist tackle the scarcity of expert professionals wanted to handle trendy, complicated know-how stacks. The corporate focuses on simplifying information 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 information sources as a substitute of driving innovation. We noticed a possibility to create an AI-powered ITOps engineer—Hawkeye—that might immediately pinpoint IT points, scale 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 other than conventional IT automation instruments?
In contrast to static, rule-based IT automation instruments, our AI-powered digital teammate, Hawkeye, dynamically processes huge telemetry information and diagnoses points immediately. It eliminates the bias of pre-programmed observability instruments by pulling insights from various enterprise information sources—together with Slack, cloud companies, 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 means of a conversational interface, diagnosing root causes and proposing fixes to complicated IT points. This essentially modifications how IT operations work, serving to them decrease downtime and reply to IT incidents with unprecedented pace.
Enterprises usually battle with information overload in IT operations. How does Hawkeye filter by means of huge information units to offer actionable insights?
Conventional IT instruments battle to course of the flood of telemetry information—logs, system metrics, and cloud efficiency indicators—resulting in alert fatigue and sluggish incident decision.
Hawkeye cuts by means of the noise by repeatedly analyzing real-time information, 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 staff, it interprets IT telemetry and system information 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 occasions from days to minutes.
Hawkeye’s distinctive strategy leverages the facility of LLMs to information incident evaluation with out ever sharing buyer information 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 information with LLMs, making certain a considerate and safe strategy.
Hawkeye operates inside an enterprise’s safety perimeter, utilizing solely inner information sources to generate insights—eliminating hallucinations that plague generic LLM-based methods. It additionally ensures transparency by offering traceable suggestions, so IT groups preserve full management over decision-making. This strategy makes it a dependable and safe AI teammate slightly than a black-box resolution.
How does Hawkeye combine with current IT infrastructure, and what does the onboarding course of seem 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 modifications.
Right here’s the way it works:
- Deployment: Hawkeye is deployed inside your setting, connecting to current instruments and information sources.
- Studying & Adaptation: It analyzes historic incidents and real-time telemetry to know regular system operations and establish patterns.
- Customization: The platform adapts to enterprise-specific workflows, tailoring responses and proposals to operational wants.
- Collaboration: By means of 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, slightly than replaces, human IT professionals. IT groups nonetheless drive strategic selections, however as a substitute of manually troubleshooting each concern, they work alongside Hawkeye to diagnose and resolve issues sooner. As AI teammates grow to be extra superior, IT professionals will shift towards higher-value duties—optimizing architectures, bettering safety, and accelerating new know-how adoption.
Hawkeye claims to scale back imply time to decision (MTTR) by 90%. Are you able to share any real-world examples or case research that exhibit this affect?
A nationwide grocery retailer built-in Hawkeye to deal with the rising complexity of its e-commerce platform. Their SRE staff was overwhelmed by huge telemetry information and sluggish handbook investigations, particularly throughout peak procuring intervals.
With Hawkeye as a GenAI-powered teammate, they noticed:
- ~90% MTTR discount – On the spot information 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 procuring 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 gives 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 corporations at present?
Entrepreneurs ought to deal with fixing actual, high-value issues slightly 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 a substitute of being inflexible, one-size-fits-all options. Moderately 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 corporations that prioritize scalability and long-term affect over short-term developments will finally emerge as leaders within the area.
Thanks for the good interview, readers who want to study extra ought to go to NeuBird.
