Julius AI

What it is

Julius is a chat interface bolted onto a Python data-science runtime. You upload a file — a CSV export, an Excel model, a survey dump — and ask questions in plain English. Behind the scenes it writes and runs pandas/matplotlib code, then hands you back the answer, the chart, and (if you ask) the code it ran. For a PM this is the shortest path from “I have a messy export” to “I have a defensible number,” without opening a spreadsheet or pinging an analyst.

Our verdict

This is our pick for the PM who works in files, not warehouses. It handles the annoying middle 80% — deduping, pivoting, correlation checks, cohort splits, quick regressions — in a conversation, and it shows its work so you can sanity-check the logic. The free tier is only good for a test drive; Plus at $20/mo is the real entry point and gives you enough headroom for weekly analysis.

Where it falls short: the 250-message cap on Plus disappears faster than you expect — a single thorough analysis burns 20–30 messages, so heavy weeks push you toward the $45 Pro tier. And like any LLM-driven tool it will occasionally pick the wrong statistical approach with total confidence; if you don’t read the code it generates, you can ship a wrong answer that looks polished.

Get value in 10 minutes

1. Sign up (free tier is fine to start) and drag in a CSV or Excel export — a product usage dump, a churn list, a pricing survey.
2. Ask the plain question first: “What are the columns, how many rows, and are there any obvious data quality problems (nulls, duplicates, weird outliers)?”
3. Then the real one: “Split users into weekly signup cohorts and show week-4 retention for each. Show me the code you ran and flag any assumptions you made about how to define ‘retained’.”
4. Read the code block it returns before you trust the chart. If the “retained” definition is wrong, say so and it re-runs.