Julius AI lands $10M for no‑code analytics

Julius AI, a no‑code platform that turns plain‑language prompts into live data analysis, has closed a  $10 million seed round led by Bessemer Venture Partners. The cash infusion coincides with the public debut of the Data Connectors, which enable a real-time analysis suite that links business-grade databases to the browser in minutes—no SQL required.

The tandem milestones cap a remarkable two‑year climb. More than 2 million users and 10 million visualizations have already flowed through the tool, while site analytics show 1.24 million monthly site visits averaging eight minutes apiece. That traction underlines why AI startups command 42% higher seed valuations than their non-AI peers this year.

Funding Oversubscribed Seed Round

The oversubscribed round drew participation from Horizon VC, 8VC, Y Combinator, and AI Grant, boosting Julius’ total raise to $10.5 million since its 2022 pre-seed. Its cap table also features angel investors Aravind Srinivas, Guillermo Rauch , and Jeff Lawson, operators behind Perplexity, Vercel, and Twilio.

Bessemer’s convictions run deep; the firm announced Bessemer’s $1 billion AI commitment last year to back builders who translate machine learning into everyday software.

Across the market, venture firms poured a record‑breaking $45 billion into generative‑AI investment in 2024, nearly doubling 2023’s total. Carta data pegs the median AI seed round at $17.9 million, well inside Series A territory for classic SaaS companies.

Plug‑and‑Play Data Connectors

Direct hooks to PostgreSQL, Snowflake, BigQuery, Google Drive, and OneDrive enable analysts to skip CSV exports and interrogate production tables from the same chat window that generates charts. The Feature launched on 28 July 2025 and rolled out to every paid tier within hours of the funding news.

Julius says the connectors cache frequent queries, respect row-level security, and throttle heavy workloads to avoid rate-limit surprises. The system also learns each schema’s quirks over time, automatically surfacing join suggestions and calculated fields.

Classroom Proof at Harvard

Academic validation arrived in March when Harvard Business School adopted Julius for its required MBA course, “Data Science and AI for Leaders.” During blind trials, the platform outperformed ChatGPT, Claude , and Gemini on messy sales data exercises.

The pilot logged 90% engagement with 840 users, who generated 8,314 conversations and 71,675 messages—enough to persuade faculty to expand the test this autumn. “Students moved from querying to insight in minutes, not hours,” said course co‑chair Professor Karim Lakhani.

From Prank to Platform

Chief executive Rahul Sonwalkar started his career as an engineer at Uber and Facebook before he graduated from the University of Texas at Dallas in 2019. Internet culture remembers him for the ‘Rahul Ligma’ Twitter prank that lampooned mass layoffs during Elon Musk’s takeover, a stunt that briefly eclipsed his code.

Before founding Julius, Sonwalkar ran a developer‑matching network that grew to 17,000 coders worldwide.

Usage Milestones and Performance

Under the hood, Julius’ compiler now generates 4 million lines of analysis code daily, translating plain-English prompts into optimized Python, R, and SQL. The engine also writes 1 million lines every 36 hours purely to test and refactor its output, a loop the team says boosts accuracy with each pass. 

Fortune 100 retailers, regional banks, and a major Southeast‑Asian telco are already running paid pilots, though Julius has yet to disclose revenue.

Market Tailwinds

Analysts forecast the data-visualization tools market to reach $15.75 billion by 2029, with a compound annual growth rate of 13.4 percent. In parallel, the $28.11 billion no‑code market in 2024 is expanding 27 percent each year as line‑of‑business staff replace spreadsheets with drag‑and‑drop interfaces.

“In the cloud era, teams want answers the moment data lands, not after the BI queue clears,” said Horizon VC partner Ayesha Memon, whose firm re‑upped in the seed round. 

Competitive Edge

General chatbots struggle with files larger than a gigabyte, yet Julius reportedly handles datasets of up to 32 GB by streaming data in chunks. It cleans nulls, detects outliers, and suggests predictive models, then pushes results into collaborative notebooks for team review. 

Those capabilities resonated at Harvard and within early corporate pilots, where Julius claims deployment times of under 30 minutes for most workloads.

What Comes Next

Sonwalkar says the fresh capital will fund an enterprise license that features SAML, row-level security, and private-cloud deployment. A plug‑in store for third‑party statistical functions and a user‑certification programme are also on the roadmap. “For us the goal is simple: make the next million data workers feel like staff scientists,” Sonwalkar told viewers during a recent webinar. 

For now, Julius will focus on converting its vast free user base into recurring revenue while investors watch how quickly it weathers the intensifying competition. The race to embed AI directly into the world’s data stacks has only just begun. 

Key Takeaways:

• A $10 million seed round led by Bessemer highlights the deep investor appetite for applied AI.
• Data Connectors launch lets users query live databases using plain language.
• Harvard adoption shows Julius outperforming general chatbots in real coursework.
• The platform already supports 2 million users who have created 10 million visualizations.
• AI seed rounds are priced 42 percent higher than non-AI deals, even amid broader funding pullbacks.