Analyze a large CSV export with Julius AI

A large export from a CRM, a ledger or a warehouse system is usually too big to read and too irregular for a quick pivot table. Julius AI takes the file, answers questions in plain language, draws charts and shows the Python code it used, so the logic is checkable. An analysis takes about 30 minutes. The first question to ask has nothing to do with your business question: it is how clean the data is.

Prepare the file

Export to CSV and remove columns with names, phones and account numbers; files are processed on US servers, and your analysis almost never needs them. Keep an ID column if you need to trace rows back.

Look at number formats. Exports from systems configured for comma decimals and space thousand separators produce values that Julius reads as text, and a sum over text is either an error or zero. Convert those columns before uploading or say so explicitly in your request.

Tell it what the columns mean

Column names in exports are often cryptic: "amt_net", "dt_ship", "st". Write a line for each column that matters and state the decision you are trying to make. "Decide which regions to cut from next quarter's delivery schedule" gives the analysis a direction; "analyze this data" produces a tour of averages.

The prompt

Role: you are a data analyst who explains results to a manager without technical jargon. Context: the file has {{number}} rows, columns: {{columns with explanations}}. The decision to make: {{decision}}. Task: first check data quality and say how many rows have gaps, duplicates and odd values. Then answer the questions: {{questions}}. For each answer build a chart and state how many rows were used. Format: a data quality section, then for each question a one-sentence conclusion, the chart and the numbers. No long dashes.

"State how many rows were used" is the safeguard. Julius sometimes drops rows with gaps without mentioning it. If a question about all orders was answered using fewer rows than the file has, you want to know why before you draw a conclusion.

Checking the answers

Take one total from the answers, such as revenue for a region or orders in a month, and reproduce it with a pivot table in Excel or Google Sheets. If it matches, the pipeline is sound. If it does not, open the code under the answer: the filter, the grouping or the type conversion will show where the difference comes from. For finance people, reading the code is less work than it sounds; you only need to see which rows were kept and how they were added up.

Keep to three to five questions per session. Each answer builds on the previous state of the data, and a long session with many transformations is harder to audit.

What to do

1. Export to CSV and remove names, phones and account numbers.
2. Fix number formats or note them in the request.
3. Explain each column and the decision you want to make.
4. Ask Julius to check data quality first: gaps, duplicates, odd values.
5. Ask three to five questions and request a chart for each.
6. Reproduce one total by hand with a pivot table.
7. Write down the conclusions and save the code the assistant used.

A result you can use

The row count before and after cleaning is known, one total matched your manual check, and the conclusions answer the questions behind the decision.

Plans

The free plan gives a limited number of messages a month and a file size limit. The paid plan's price is to be confirmed on the vendor's site. See Julius AI pricing, the auditor's version of this task and Julius AI vs Rows AI.