Jared Zhao originally got interested in data analytics during his time at UC Berkeley because he was drawn to how it could turn raw data into a story. Zhao founded his first data analytics startup Polyture in 2021. But advancements in generative AI just a year later made Zhao realize what Polyture was building was too complicated for what users would be looking for in a post-ChatGPT world, and decided to change course.
The result was Athenic AI, a company that uses AI to run data analytics for enterprises across all of their data sources. Zhao, the founder and CEO, said that Athenic’s products are designed to be a central nervous system of an organization’s databases that can be used by anyone in the company regardless of their coding or data experience.
Zhao (pictured above in the center) added that Athenic is built to be flexible and can work with companies to get its AI to understand company “tribal knowledge”, KPIs or internal terminology so that the AI has the needed context to run proper analytics.
Each data report the AI-driven system pulls includes an explanation of how the AI interpreted the data which makes it easier for users to spot potential errors and give the AI model feedback. Zhao added that this helps with visibility and that while they want the AI to get as close to 100% accuracy as it can, human data analysts can’t reach 100% accuracy either.
“Even when the system is wrong, it is aware that it might be wrong, and it explains to the user why it thinks that it could be wrong,” Zhao said. “And that’s what a good data analyst does. They don’t just give you the report or the chart, they also give you an executive summary that explains how you should interpret this and what they did to do this analysis.”
The company was founded in 2022 and launched it’s product in Summer 2022. Since it launched, Athenic has been able to land customers ranging from small startups to large enterprises including Additel and PMC. Zhao said that the company has found many of its smaller customers through outbound sales leads but that the majority of their enterprise clients came from inbound interest.
San Francisco-based Athenic is now announcing a $4.3 million seed round led by BMW i Ventures with participation from TenVC, Scrum Ventures and Stage 2 Capital, among others. Zhao said the money will be put toward hiring and building out new tech capabilities.
“Today, the user asks questions and pulls the insights out of the system that they want to see,” Zhao said. “There’s also a world where the data has some kind of insight that’s innate to the data that we’ll want to suggest to the user before they even ask.”
Samantha Huang, a principal at BMW i Ventures, told TechCrunch that she got introduced to Athenic in kind of a random way. Huang said that her firm decided to get a better feel of the AI startup ecosystem in general and “boiled the ocean” by reaching out to as many AI startups as they could to get a vibe check.
Athentic was one of them. Huang said the company stood out from other data analytics companies because of the fact that it helps companies get the AI models set up with company-specific context and knowledge.
“A lot of companies will use these generic, monetized, foundational models, but the problem is, technically, the model, it’s kind of dumb if you don’t know what the data in the customer’s environment looks like,” she said. “Jared took a new approach, combining a knowledge graph plus foundational models that allowed him to bridge that problem.”
The data analytics market is crowded, and likely will get increasingly so as generative AI improves and more companies look to capitalize on how AI can improve the management and use of their data. Databricks is just one example in this sector that has raised more than $19 billion in venture capital and is currently valued at $62 billion. There are also numerous data storage and optimization-focused companies that could easily expand into that space.
Zhao thinks the companies approach of focusing heavily on user experience and ensuring that the AI models have the proper company context helps set them apart.
“We just think that there’s too many businesses that are being run without the proper knowledge, even though all the data is technically there,” Zhao said. “Folks at the top sometimes, not out of ignorance, a lot of times are flying blind, and that’s the problem that we really want to solve.”
