Fiveonefour is building an open-source developer framework for analytical backends, AI tooling, and the infrastructure needed to help developers rapidly productize and operationalize data. Pulse 2.0 interviewed Fiveonefour CEO Tim Delisle to gain a deeper understanding of the company.
Tim Delisle’s Background
Could you tell me more about your background? Delisle said:
“Absolutely. I’m one of the co-founders and CEO of Fiveonefour, where we’re building an open-source developer framework for analytical backends, AI tooling, and the infrastructure needed to help developers rapidly productize and operationalize data.”
“Before this, I founded and led a company called Datalogue, which was acquired by Nike. After the acquisition, I joined Nike to lead Global Data Engineering. It was a great experience and it also exposed a lot of the challenges even top-tier organizations face when it comes to deriving value from data. That experience really shaped the vision for Fiveonefour.”
“Throughout my career, I’ve been lucky to work with great teams, ship meaningful products, and now I also spend time advising startups, venture funds, and nonprofits, especially around data, infrastructure, and go-to-market.”
Formation Of The Company
How did the idea for the company come together? Delisle shared:
“The idea really came together during my time at Nike. While leading Global Data Engineering, even at that scale, with all the resources in the world, I saw firsthand how hard it was to build scalable, developer-friendly data systems.”
“The teams were incredibly smart and driven, but they were constantly running into complexity, bottlenecks, and tech that just wasn’t built with developers in mind. That experience made it clear: if this was the state of data infrastructure at a company like Nike, then there was a huge opportunity to build something better.”
“That realization became the foundation for Fiveonefour, giving teams the frameworks, infrastructure, and AI tools they need to ship real-time, data-driven features without all the friction.”
Core Products
What are the company’s core products and features? Delisle explained:
“At Fiveonefour, our core products are all designed to help developers build best-in-class analytical backends quickly and without unnecessary complexity. Our open-source framework, Moose, makes it easy to build analytics features and data APIs using languages developers already know, like TypeScript or Python. Boreal is our one-click hosting platform for Moose projects, complete with CI/CD, observability, and built-in security, so teams can move from development to production seamlessly. And then there’s Aurora, our AI-powered agent that automates much of the heavy lifting in data engineering, from generating transformation logic to helping debug pipeline code. Together, these tools make it drastically easier to go from raw data to polished features in a fraction of the time.”
Challenges Faced
What challenges have Delisle and the team face in building the company? Delisle acknowledged:
“Building for the future in the incredibly dynamic AI landscape is terribly challenging. Every iteration cycle from the big AI labs brings a monumental leap in model performance, and keeping up with these requires a steadfast focus on agility and speed. Our main solution to this problem has been to push ourselves and our teams to have clarity in vision while staying fast and nimble in this dynamic environment.”
Evolution Of The Company’s Technology
How has the company’s technology evolved since launching? Delisle noted:
“Since launching, the biggest evolution in our technology has been the introduction of Aurora, our suite of AI agents for data engineering. When we started, the focus was on Moose and Boreal, giving developers better frameworks and infrastructure to build analytics features fast. That alone was a huge unlock. But over time, we realized there was an even bigger opportunity: using AI to actually do the work, not just accelerate it.”
“That’s where Aurora comes in. It’s a set of AI-powered agents that can generate pipeline code, create production-ready APIs, handle streaming transformations, and even help debug issues in real time. We see this as a natural progression, just like software ate the world, we believe agents are going to eat software. The companies that win won’t just build with AI; they’ll build as AI, delegating entire layers of complexity to agents that can work alongside developers.”
“So while we started with tools that made developers more productive, we’re now building systems that can do the job with them, or even for them. It’s a huge shift, and we’re just getting started.”
Significant Milestones
What have been some of the company’s most significant milestones? Delisle cited:
“One of our biggest milestones has been the launch of Aurora into research preview. It represents a major step forward in our vision, moving from simply accelerating development to enabling AI agents that can actually do the work of data engineering. The response from early customers has been incredible. They’ve been especially blown away when they see Aurora combined with our Model Context Protocol (MCP), which gives the agents deep, structured understanding of the data environment. It unlocks a level of automation and precision that’s hard to achieve otherwise.”
Customer Success Stories
When asking Delisle about customer success stories, he highlighted:
“One great example is our work with F45 Training, a global fitness franchise with over 1,500 studios and a huge base of connected fitness users through their Lionheart platform. They wanted to give members richer, real-time insights into their workouts but their legacy infrastructure couldn’t handle the scale or latency demands. With Fiveonefour, they rebuilt the entire user-facing analytics layer in just a few weeks using Moose, Boreal, and Aurora.”
“The results were pretty remarkable: they increased app engagement by 19%, virality by 44%, and member satisfaction by 70%. Their engineering team also 10x’d their development speed for analytics features, and cut infrastructure costs by 50%. It’s a perfect example of how real-time, personalized data experiences can drive both user impact and business outcomes, especially when the underlying stack is built for speed and scale.”
Funding
When asking Delisle about the company’s funding details, he revealed:
“We’re well funded and in a strong position to execute. Our tools are already in production powering everything from publicly traded companies to early-stage startups. We’re focused on building real, long-term value, and the traction so far has been really encouraging.”
Total Addressable Market
What total addressable market (TAM) size is the company pursuing? Delisle assessed:
“We’re targeting a segment of the global data infrastructure and analytics tools market, which is expected to exceed $100 billion by 2030. Fiveonefour focuses specifically on the intersection of real-time, operational, and developer-first infrastructure, a space we estimate to represent a $10–20B opportunity, with significant upside as the category matures. It’s a fast-growing area, driven by the shift toward embedded, real-time data experiences and the rising demand for tools built with developers in mind.”
Differentiation From The Competition
What differentiates the company from its competition? Delisle affirmed:
“What really sets us apart is that we’re fully developer-first, not just in messaging, but in how everything is built. Most data platforms are made for analysts and retrofitted for engineers. We built Fiveonefour from the ground up for developers who ship real products.”
“With Moose, you can build real-time data features in TypeScript or Python. Boreal handles one-click deploys with full CI/CD. And Aurora brings in AI agents that actually help write and manage your data workflows.”
“It all fits cleanly into the dev stack, no new languages, no weird tools. Just fast, flexible infrastructure built for teams that move quickly and want to own the full data layer.”
Future Company Goals
What are some of the future company goals? Delisle concluded:
“One of our core goals is to fundamentally shift who can build with data. Our job will be done when “data engineering” as a separate discipline doesn’t need to exist because any software engineer with basic TypeScript or Python skills can do the work of 10 data engineers using our tools.”
“We’re focused on continuing to push the boundaries of what AI agents can do in this space, automating more of the heavy lifting, reducing complexity, and making real-time, production-grade data features something any developer can ship quickly and confidently. The long-term vision is a world where building with data is just part of everyday software development, not a specialized bottleneck.”
