Transforming the way business and data teams collaborate

We’re thrilled to announce that Eniac Ventures, Outlander, and Recall Capital have invested $3M in to transform the way data teams operate.

Lei and I have spent our careers in the SaaS analytics and AI space. The AI world has seen its share of false starts, but when we saw the latest generation of LLMs, we realized that this time would be different, and we immediately saw the potential to solve a huge pain point that we’ve both experienced firsthand, both as product and data science leaders: Using data to make decisions is critical, but if the question hasn’t been asked before in the exact same way, product, revenue and customer success teams have to resort to reaching out directly to the data team and hope that they can spare a bit of time to pull the data. More often than not, this is a report that can be pulled using an SQL query, as long as you know how to write SQL and you have a good grasp of the data model — but unfortunately, that doesn't apply to most people outside the data team.

We believe that this is a fundamentally broken process, which is forcing business teams to be less data-driven than they would like to be, while also becoming a huge productivity drag on data teams, who would rather spend their limited time working as strategic partners to the rest of the company and focusing on critical company-level initiatives.

The latest generation of LLMs is extremely powerful and well-placed to translate plain English questions into SQL code. However, corporate data is messy, sensitive and follows a certain data model that often lives in the heads of the data team. At our mission is to fix this broken process and transform the way data teams operate, using generative AI to simplify the interface to data, while providing the data team with control over the output, guaranteeing total trust in the system. In this new world, anyone will be able to ask questions of their data in an intuitive conversational interface, and collaborate with the data team on the more complex questions.

"I was able to get insights in 1/10th of the time it normally would have"

Don't take our word for it, give it a try!