For much of my career, I’ve been driven by a simple vision: business users should be able to ask a straightforward question—something like “What were the sales of white pull-on loafers in Malaysia this morning?”—and get an answer instantly. That vision first started to come together during my time at Zoomdata, though we quickly realized that achieving it required solving deeper challenges around query performance, natural language querying (NLQ), large language model (LLM) governance, visualization, and scalability.
I’ve been fortunate to have worked for companies and partners that have tackled different parts of this puzzle, and to play a role in pushing technology and ideas forward. Now, I’m thrilled to be joining a company that’s on the cutting edge of applying AI Analytics to the enterprise and building the definitive AI platform necessary to finally deliver on that vision.
At my previous company Zoomdata, our goal was to create an interactive analytics platform that delivered insights in real time. Our first application was built for the iPad, designed to let users orchestrate analytical experiences against billions of data points with a simple, tactile interface. An early startup lesson? Selling an iPad-only app to enterprises meant that we often had to buy the iPads for the users that used Zoomdata.
A core foundational technology was Zoomdata’s library of big data connectors, which supported push-down queries and eliminated the need to forklift data into proprietary caches. Our micro-query architecture minimized data movement and enabled streaming analytics.
Our pitch to customers was simple: “Why wait until tomorrow to find out what’s happening now?” That message resonated, especially with one large pharmaceutical customer who analyzed seven billion rows of data in seconds from an iPad, all through a beautiful, self-service experience. Still, Zoomdata’s self-service approach depended heavily on the user’s knowledge of the data and the ability to endlessly filter their way to insights.
When I joined Tellius after Zoomdata, I quickly saw the power of their analytical engine. Their natural language query (NLQ) interface converted text into SQL, making dashboards and visualizations more intuitive. Users could manipulate data through a Google-like interface, even using voice commands.
But the real breakthrough was their application of Machine Learning to the analytical experience. Built on Spark, Tellius applied machine learning techniques to tasks like anomaly detection, cohort comparisons, and segmentation. Instead of requiring users to slice and dice data endlessly, Tellius enabled the data itself to tell the story. Since my time there, they’ve continued to innovate, weaving even more AI into their platform.
Most recently, I worked with Pyramid Analytics, whose highly rated Gartner-recognized ABI platform integrated data prep, analytics, and machine learning into a single, powerful experience. Pyramid solved many of the hard problems that traditional hyperscalers like Power BI, Looker, and Tableau couldn’t address.
One standout feature was their application of AI to dashboarding. Users could ask broad, natural language questions without worrying about syntax, and the system would leverage LLMs to interpret intent, generate queries, and update dashboards automatically.
Now, I’m stepping into a new role as Head of Sales at Redbird. After meeting co-founders Erin and Deren Tavgac, I was impressed by how the team has assembled a platform that leverages AI agents to handle the full data lifecycle—from Data Prep, to Analytics, to Data Science, and Reporting.
Here’s what that means: when a user asks, “What were the sales of white pull-on loafers in Malaysia this morning?”, Redbird agents automatically identify the source data, build and harmonize the pipeline, analyze it, and deliver the answer. Even better, Redbird AI agents can train and run predictive models in response to user chat interactions and help users anticipate and act on trends in their data. This is transformative for enterprises - users can finally have a conversation with their data on-demand and without limitations based on technical ability, which will usher in the era of truly democratized analytics and data-driven decision making..
AI Analytics fundamentally changes the BI paradigm. Instead of starting with dashboards and filters, users start with natural language chat. The results are shaped directly by a user’s intent, not by pre-built, rigid structures.
I’m thrilled to join Redbird at such an exciting time. They’ve been working on this problem set for years, building a strong customer base eager to move beyond the traditional limitations of dashboards and infinite filters. And what excites me most is that Redbird doesn’t just analyze data—it also takes action, using AI agents to respond to insights in real time.
It’s going to be a wild ride as this new generation of tools redefines how businesses connect, analyze, and act on their data.