Keebo AI Optimizes Data Warehouses With Automated Learning Platform, Raises $10.5M

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With the rise of Snowflake and other cloud data warehouses, companies finally have an easy way to mobilize their data assets at scale. They can easily connect data from different sources and start increasing efficiency while keeping initial investment (or CapEx) on the low side.

The benefits of the solutions are unparalleled, but cloud data services also pose the challenge of high operating costs. With continuously growing datasets, companies face high compute costs and query performance latencies. Without a solution, their teams must spend about 30-40% of their time manually developing features that can optimize the warehouse for required performance and budget constraints.

The time invested in manual processes consists of technical resources that could otherwise be used in other critical areas with clear business value, such as DataOps and cloud infrastructure.

Keebo automates data warehouse optimization

To rise to the challenge, Michigan based Keebo AI provides a data learning platform that makes the entire process of optimizing the cost and performance of data warehouses intelligent and automated. Today, the company announced $10.5 million in a Series A funding round.


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“Keebo…builds a series of ‘smart models’ by learning how users and applications interact with data over time and uses those smart models to automate and speed up the tedious aspects of those interactions,” says Barzan Mozafari , CEO and co-founder of Keebo. “…For example, instead of just answering the user’s question, Keebo learns from each question how to speed up subsequent searches, even if those questions are seemingly very different in nature. And it does all this learning by simply looking at performance logs and metadata rather than the actual customer data.”

The turnkey and drop-in solution first debuted in 2021 as a semi-automated on-premise beta offering. Now, with this round, the company has expanded into fully automated warehouse optimization. The hosted capability, the company claims, can quickly identify opportunities to make data processing more efficient with less cloud usage, helping companies reduce the cost of their cloud warehousing by 30-60% on average.

Keebo claims that his platform has already helped many companies speed up their analytics queries by up to 100 times. Its customer base includes players like Allbirds, TUI, Barstool Sports, PayJoy, 14 West, HyperScience, and Dr. squat.

“Keebo takes care of the things I don’t want to think about or deal with,” said Trish Pham, chief analytics officer at PayJoy. “It requires no work on my part. Even if I loved manual optimization, I couldn’t possibly achieve what Keebo achieves automatically. I log in for a few minutes every few weeks to see what Keebo saves us. I used to spend hours a week doing manual optimizations.”

The entire deployment effort of the platform takes 30 minutes from start to finish, which usually involves creating the user and pointing the platform to the workloads to be optimized. It’s “set and forget”, meaning there is no additional deployment maintenance or learning curve after the initial onboarding. Users can also go in and set their goals, such as reducing compute costs or speeding up queries in the data warehouse.

Plan ahead

With this funding round, which was led by True Ventures, Keebo’s total capital raised is now $15 million. Mozafari said the company will use the new round to grow its team and further improve the product with three core capabilities.

“First, we want to close the gap between optimized and optimal performance. Second, we plan to expand our learning platform to support a wider range of data warehouse stacks. And third, we plan to shorten the time-to-value for our customers from 24 hours to three hours,” he added.

Globally, the data warehousing market is expected to to grow from $21.18 billion in 2019 to $51.18 billion in 2028.

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