Jared Zhao first became fascinated with data analytics during his time at UC Berkeley. What drew him in was how raw numbers could be transformed into a compelling story—turning seemingly meaningless data into insights people could understand and act on. In 2021, he launched his first startup, Polyture, focused on data analytics. But just a year later, the rapid rise of generative AI, especially tools like ChatGPT, made him realize that Polyture’s approach was too complex for what users would expect in this new era.
So, Zhao pivoted and founded Athenic AI, a company designed to automate data analytics across all enterprise data sources. As founder and CEO, Zhao envisions Athenic as the “central nervous system” for a company’s databases—an AI-powered platform that anyone in the organization can use, no matter their coding skills or data background.
Making Data Speak Human
Zhao emphasizes that Athenic aims to democratize data analysis. Traditionally, data insights are the realm of specialized teams who sift through complex reports, leaving the average employee waiting days for answers. Zhao believes data systems should be like a smart colleague—ready to answer any question in everyday language and understand the company’s unique context.
Take Additel, a German manufacturing company and one of Athenic’s clients. Before, the marketing team had to request reports from IT and wait days to get insights for monthly sales strategies. Now, they simply type questions like, “What caused the sales drop in Western Europe last month?” Athenic’s AI pulls relevant data, explains the reasoning, and highlights which data points were key. If the data seems off, the system even flags potential issues and explains why.
“We’re not trying to replace human analysts,” Zhao says. “Instead, we want everyone to be able to interpret data like one. A good analyst doesn’t just hand you charts; they also provide an executive summary explaining how to read them.”
Cracking the Code of Company “Dialect”
One big challenge companies face is their internal “tribal knowledge” or unique jargon. Every organization has its own terminology and key performance indicators (KPIs) that outsiders—and even generic AI—can misunderstand. Zhao highlights that Athenic’s strength lies in its ability to “learn” this company-specific language.
For example, PMC, a major U.S. engineering firm and Athenic customer, uses the acronym “PCE” to refer to a core business process. But in other contexts, “PCE” can mean something completely different. Generic AI models often get confused by this, but Athenic works closely with clients to embed these unique definitions into its analytics.
Samantha Huang, principal at BMW i Ventures, shares how her firm discovered Athenic during an extensive outreach to AI startups. She points out that while many companies deploy generic AI models, they often fail because “the model is dumb if it doesn’t understand the data environment.” Huang praises Zhao’s approach of combining knowledge graphs with foundational AI models, effectively giving the AI a company-specific “dictionary” to truly grasp context.
Standing Out in a Crowded Market
The data analytics space is already packed, and with the rise of generative AI, competition is heating up. Databricks, for instance, has raised over $19 billion and is valued at $62 billion. Many companies focused on data storage and optimization are also moving into analytics.
Yet, Zhao isn’t worried. He believes Athenic’s focus on user experience and contextual understanding sets it apart. Smaller companies, which often lack dedicated data teams, need an AI assistant that “speaks business.” Larger enterprises struggle with data silos and cross-department collaboration—areas where Athenic aims to bridge gaps.
Zhao shares a real-life scenario: a startup preparing for fundraising needed quick access to critical operational data to draft their pitch. Previously, they would have waited a week to gather info from multiple departments. With Athenic, the founder simply asked, “What’s been our customer retention and CAC trends over the past six months?” Within minutes, the system provided a detailed report and insights—saving valuable time.
From Reactive to Proactive Insights
Today, Athenic’s platform mainly responds to user questions. But Zhao has bigger ambitions. He envisions a future where the AI proactively spots trends and anomalies, alerting users before they even ask.
“Think of how Netflix recommends shows you didn’t know you wanted to watch,” he says. “We want data systems to do the same—surfacing hidden insights automatically so businesses can act faster.”
Funding and the Road Ahead
Founded in 2022 and based in San Francisco, Athenic has grown steadily, serving both startups and large enterprises. Zhao notes that smaller clients mostly come from outbound sales, while bigger companies tend to reach out on their own.
Recently, the company announced a $4.3 million seed round led by BMW i Ventures, with participation from TenVC, Scrum Ventures, and Stage 2 Capital. Zhao plans to use the funds to expand the team and enhance technology capabilities.
In a world where data is everywhere but understanding it isn’t, Athenic aims to be the bridge that transforms raw numbers into meaningful conversations for everyone in the company.
“Many businesses aren’t lacking data—they’re lacking understanding,” Zhao says. “We want to empower people to talk to data, no coding required, and make smarter decisions together. That’s the future we’re building.”