Julius AI secured a $10M seed led by Bessemer to scale its natural-language data analyst platform. The deal validates an ever growing market which is AI tools for data analysis at organizations of all sizes. However, our team deeply believes data is only good if there is trust in it, and we believe that means engineers need to be part of the data exploration workflow.
Venture capital just sent a clear message: the era of specialized AI analysts has arrived. Julius AI, a startup that turns natural-language questions into code, visualizations, and forecasts, closed a $10 million seed round led by Bessemer Venture Partners.
The raise crystallizes a broader shift from generic chatbots toward vertical AI applications that solve one painful job inside the enterprise data stack – answering ad-hoc questions without waiting for a human analyst.
The question is, is that enough, and is removing the analyst / engineer a good thing? We have tons of thoughts here at Galaxy!
Founded in 2022 and graduated from Y Combinator, Julius claims more than two million users who have generated ten million visualizations. The product lets managers ask questions like “Show revenue vs. net-income correlations by industry” and instantly returns charts and predictive models.
The seed syndicate includes Horizon VC, 8VC, YC’s AI Grant, and angels from Perplexity, Vercel, and Twilio. Harvard Business School already white-labeled Julius for its Data Science and AI for Leaders course, highlighting early enterprise traction.
Investors are betting that thin but deeply specialized layers will capture workflow value above foundational models. Julius rides this thesis by focusing laser-like on data analysis rather than broad Q&A.
Traditional BI and notebook tools require SQL expertise and manual charting. An AI that writes code, selects models, and explains results in plain English threatens to compress those steps into a single conversation, forcing incumbents to add copilot features.
If Julius and similar tools mature, companies can redeploy analysts from one-off requests to higher-order modeling and governance. That reshapes hiring plans and shifts budget toward AI copilots plus data quality initiatives.
When every employee—technical or not—can query trustworthy data on their own terms, the entire organization moves faster, learns more, and wastes less. That democratization unlocks compounding advantages: sharper decisions, tighter feedback loops, and a culture that treats data as a first-class product rather than a black-box service.
Ultimately, most “self-service” analytics platforms (even those like Julius) chase ease-of-use by hiding SQL behind drag-and-drop layers or autogenerated code. In doing so, they also sideline the very people who understand the data model best—analytics engineers, data engineers, and power users. Absent their context and tribal knowledge, metrics drift, dashboards lie, and trust erodes. A query built without the experts in the loop is maybe correct at launch and probably wrong after the next schema change.
Rather than abstracting SQL away, Galaxy puts it—along with the experts—at the center of the experience.
• A $10M seed at the concept stage signals oversized investor confidence in vertical AI analysts.
• Julius’s traction proves that natural-language data exploration resonates far beyond technical users.
• BI and notebook incumbents must ship trustworthy copilots or risk rapid share loss.
• Galaxy is excited to build an even stronger data foundation by building for developers and sharing data across the organization.
Expect a land-grab where vertical AI startups target every spreadsheet-driven workflow: finance FP&A, supply-chain planning, healthcare coding, and more. Funding will favor teams with proprietary data or tight feedback loops that improve model accuracy.
Enterprises will demand governance layers that validate AI-generated queries before they hit production databases. Vendors that combine chat UX with compliance and lineage – Galaxy among them – will win long-term.
Julius AI’s seed round is not just another funding headline; it is the canary for a platform shift. Domain-specific copilots are moving from novelty to necessity, collapsing the distance between question and insight.
For data leaders, the mandate is clear: adopt tools that let business users self-serve while keeping experts in the loop. Ignore the trend, and your analysts may be chatting with recruiters instead of stakeholders.
Check out Galaxy today for a data-first approach to querying your databases with our context-aware AI copilot :)
.avif)
.avif){kind=logo}
