Riding the Waves of Enterprise Data Growth with GenAI: Announcing NeuBird’s $22M Seed Round

Everything comes in waves, and we are well aware of the tidal surge in the growth of enterprise data. In the ever-evolving landscape of enterprise data management, organizations have faced a series of transformative waves, each posing unique challenges and opportunities.

Amid these waves, I’m energized to announce a significant milestone for our team: NeuBird has raised a $22M seed round, led by Mayfield. This new capital boosts our commitment to navigating these challenges and embracing the opportunities, propelling us forward as we bring the first AI ITOps engineer to the enterprise.

Story Time: A quick recap of what happened to Enterprise data from the mid 2000s:

1. 2000s: We needed to handle more data than a human can summarize — so we invented visualization tools.
2. A decade later — we couldn’t effectively store this much data — so we found ways to optimize the data with dedupe and created warehousing solutions.
3. Another decade later we needed to learn and react from the data stored — so we created machine learning platforms on these data lakes.
4. 2020’s: Human power is coming to a breaking point when it comes to dealing with the endless growth in enterprise data. We have GenAI now, but how does this apply to the current data wave?

The issue now isn’t about how you visualize such a huge amount of data anymore — or even store it for that matter — we know how to do that well. It is now about what actions you can take from it. And I’ve been a data analyst most of my life. During my career, I’ve been hands-on involved with QA, automation, customer support, troubleshooting live environments and been under the gun to get resolutions. We do this by looking at IT telemetry (alerts, metrics, logs, traces), dashboards, writing queries and correlating information and making decisions.

We can’t do it like this anymore. Why?

Here is why: Over the past decade we have optimized how we build modern applications using cloud native methodologies — services oriented architectures. All for the greatness of building modular code in an agile way.

But this came with a side effect of too many layers and therefore too much telemetry to diagnose and troubleshoot the three main operational issues:

1. Performance
2. Crashes and outages
3. Optimization of resources

And the modern IT stack is a horizontal puzzle with many disparate sources of information that need correlation. A human operator cannot possibly process so much information in a timely manner to take action — bringing reactive IT Operations to a breaking point.

However GenAI can — And we can work alongside a new Digital Employee that is born in the age of GenAI. One that can scour through the data that we can’t. And one that can take real time decisions based on generative models. Using such models to analyze the environment and then seek the needed information to create a hypothesis and therefore a solution is the only way forward.

I have lived this life as a CTO, architect, and a customer satisfaction engineer at my previous companies. I have seen first hand how our customers struggled with their sprawling application infrastructure. We helped them troubleshoot layers of APIs as new architectures emerged in the cloud native era. It became cumbersome to sift through so many Metrics, Alerts, Logs, Traces — and correlate them with application configuration changes. All this can be handled by GenAI — by NeuBird.

The past two years have taught us how LLMs can create content. But we are using them in a unique and converse way to process content. To process loads of telemetry that no human should have to.

My Point: Accept it or not, a part of our next workforce is going to be born in the GenAI era — A digital workforce. What we have to ask ourselves is how we classify this. Are they platforms? Are they agents? Are they tools? Are they our colleagues?

We had AI for the past few decades — but these were more static and brittle. They were rigidly trained systems.

But GenAI is different because it is based on human-like cognitive models that are trained to always generate an opinion. We can use this in a controlled manner for human tasks. We are at a point where we need to start re-imagining the future of ITOps.

As we look to the future, NeuBird is poised to lead the charge in redefining how enterprises manage their increasingly complex IT stacks. With the support of our partners at Mayfield, our team is not just responding to the current wave of data — we are creating the next wave. Our goal is to empower enterprises to not only keep pace with technological advancements but to stay ahead, making real-time, informed decisions that drive success. We’re excited to be at the forefront of this evolution, and we look forward to the time savings and innovation our platform ushers in.