Data analytics startup Athenic AI wants to be an enterprise’s central nervous system

Jared Zhao was originally interested in data analysis during his time at UC Berkeley because he was attracted to how the unprocessed data could turn into a story. Zhao founded his first data analysis start-up polet in 2021. But progress in Generative AI let Zhao realized only a year later what polyture was building was too complicated for what users would look for in a post-chatgpt-world and decided to change the course.

The result was Athenic AIA company that uses AI to perform data analysis for companies in all their data sources. Zhao, the founder and CEO, said that Athenic’s products were designed to be a central nervous system of the databases of an organization that can be used by anyone in the company, regardless of their coding or data experience.

Zhao (shown above in the middle) added that Athenic was built to be flexible and can collaborate with companies to make his AI understand to understand the company “tribal knowledge”, KPIs or internal terminology, so that the AI ​​the required context has to perform the right analyzes.

Each data report that the AI-driven system adopts contains an explanation of how the AI ​​interpreted the data, making it easier for users to recognize potential errors and to give the AI ​​model feedback. Zhao added that this helps with visibility and that although they want the AI ​​to get almost 100% accuracy if possible, human data analysts cannot achieve 100% accuracy.

“Even when the system is wrong, it is aware that it might be wrong and it explains to the user why it thinks it can be wrong,” Zhao said. “And that is what a good data analyst does. They not only give you the report or graph, they also give you a summary that explains how to interpret this and what they have done to do this analysis. “

The company was founded in 2022 and launched its product in the summer of 2022. Since it launched, Athenic has been able to land customers, ranging from small startups to large companies, including additeel and PMC. Zhao said that the company found many of its smaller customers through outgoing sales leads, but that the majority of their business customers came from incoming interest.

Athenic, based in San Francisco, now announces a sperm of $ 4.3 million under the leadership of BMW I Ventures with participation of TenVC, Scrum Ventures and Stage 2 Capital, among others. Zhao said the money will be put into hiring and building new technical possibilities.

“Today the user asks questions and gets the insights from the system they want to see,” said Zhao. “There is also a world where the data has some sort of insight that is innate in the data that we want to present the user before they even ask.”

Samantha Huang, a director of BMW I Ventures, told WAN that she was introduced to Athenic in any way. Huang said her company decided to get a better feeling about the AI ​​startup ecosystem in general and “the ocean cooked” by achieving as many AI startups as possible to get an atmosphere check.

Athentic was one of them. Huang said that the company stood out of other data analysis companies because of the fact that it helps companies to set up the AI ​​models with company-specific context and knowledge.

“Many companies will use these generic, monetized, fundamental models, but the problem is technically the model, it is a bit stupid if you don’t know what the data in the customer’s environment look like,” she said. “Jared has chosen a new approach, in which a knowledge graph was combined plus fundamental models with which he could bridge that problem.”

The data analysis market is busy and will probably become more and more, so that generative AI improves and want to benefit more companies from how AI can improve the management and use of their data. Databricks is only an example in this sector that has collected more than $ 19 billion in risk capital and is currently appreciated at $ 62 billion. There are also numerous data storage and optimization-oriented companies that can easily expand to that space.

Zhao thinks that the companies are approaching to concentrate strongly on user experience and to ensure that the AI ​​models have the right business context, helps them to distinguish them.

“We just think there are too many companies that are run without the right knowledge, although all data is technical,” said Zhao. “People at the top sometimes, not out of ignorance, often fly blindly, and that is the problem that we really want to solve.”