Julius AI Review: Academic Tool for Researchers

Statistics is usually a challenge for anyone who is not a statistician, yet it’s one of the most widely used tools in science. The problem is the breadth of methods and assumptions available for most issues unknown to the novice. Even experienced practitioners rely on the help of statisticians.

Julius AI is a research tool that aims to make statistical analysis easy, intuitive and reproducible. In this review, we’ll put Julius AI to the test and explore its capabilities.

What is Julius AI?

Julius AI combines Python, Excel and ChatGPT to write code for statistical analysis and plotting. Under the hood, Julius AI takes your data and writes code to evaluate it according to your prompt. The results are plots and tables. It can also use AI to derive logical conclusions from the results. The key strengths are:

• No coding required: You can perform statistical tests and visualise data without writing Python or R scripts. Additionally, all code can be adjusted if you know a little bit about coding (and saved for later documentation).

• Fast and intuitive: Julius AI processes data quickly and can be queried in natural language.

• Automated charts and reports: Julius AI generates graphs, tables, and summaries on demand using a step-by-step “thinking” process, similar to the newest ChatGPT model, o1.

• Interprets data for you: You can get detailed explanations for results, which is helpful to understand the statistics being used and to “chat” with your data.

• Supports various data formats: You can upload CSVs and spreadsheets; Julius AI also integrates directly with Google Sheets.

Let’s review Julius AI with two real-world scenarios and evaluate how good the output is.

How does Julius AI work?

Julius AI lets you interact with, understand and visualise your data in plain English. This allows you to avoid coding when creating plots or graphs of your data, and is useful to get a quick idea of how your data is distributed.

You can use Julius AI to generate graphs, tables, and summaries for your data. The tool uses AI in order to convert your plain-text input into runnable code. In fact, you’ll be able to see how Julius AI does this, because it shows every step of its work. Then, it’ll generate the resulting visuals in two different formats (static and dynamic).

Altogether, this make Julius AI an incredibly useful and reliable choice for researchers who want to conduct initial analyses, generate plots or interact with their data without needing to program.

Julius AI Review

In this review we are going to look into two of JuliusAI’s best use cases: Visualising data and running statistical tests. Each is evaluated to see how accurately the tool performs, and give you an idea of how the output is generated.

How to plot data with Julius AI

First, I test Julius AI on it’s ability to plot data. I upload a very large data file from an earlier publication of mine. In this example, my goal is to create a visualisation that can compare the performance of a machine-learning model based on different inputs to the model (variable sets). My prompt is below. As you can see, my main question is “How does the F1 score vary with different features for each model?”. Keep in mind that F1 score is one way to measure performance of a model.

If this example feels too “techy”, don’t worry. This isn’t about my domain-specific example, but rather about looking at how Julius AI will use my data to create a visual I can [hopefully] use in my notes or even a paper.

In response to my prompt, Julius AI writes some code to generate the plots. If you are familiar with Python, this code will be pretty straightforward. I was also able to edit the code in order to add a filter to select which data from the CSV file I wanted to use. I added just one line of code below, using the “Edit code” function.

Julius AI will usually generate an interactive and a static plot. Interactive plots let you inspect the data and look at the outliers or zoom in. However, they aren’t likely to be used off the platform. The static plots (i.e. images) are for for putting into a publication.

You can even copy and save the code to your repository to document your progress.

How to run a statistical test with Julius AI

In this review of Julius AI, I conduct a second test to see how well the software can run a statistical test. I give Julius some data and ask it to find whether there are statistically significant differences between the performance of different models. The CSV file, as in the previous test, contains the performance of each model. However, this task is more tricky than the last one, because there are many different statistical tests and not all of them meet the requirements. Here is my prompt:

The AI can’t solve this in one step, revealing one of the strengths of Julius AI: step-by-step reasoning and execution. Look at the different steps it takes to achieve this task:

Interestingly, when errors in the code occurred, Julius AI would find ways to solve them and continue the execution. Julius AI works by responding to errors it experience in its own code, and adapating what it does to work. In the example above, you can see how it hit a “ModuleFoundError”, that it was able to fix and overcome.

The execution took about nine steps to finish, and I got a table summarising the statistically significant different groups and a text output of which variables are better for my model.

This is exactly what I was looking for, and it solved my problem perfectly.

What to use Julius AI for?

Recently, Julius AI introduced workflows, which are pre-saved prompts that can be used as inspiration or templates to operate on your data. These showcase what you can accomplish with the tool:

My favourite workflows include:

• Data cleaning & transformation: Quite often you will need to remove outliers or rows without entries or transform your columns into rows. Using Julius AI saves me a lot of time here.
• Extracting tables from PDFs: If you are dealing with older scientific materials, data is often transmitted in long tables inside PDFs, which are tedious to extract by hand.
• Statistical significance testing: Despite having a machine learning background, I struggle with the breadth of options available by statistical tests. Julius AI is a great help for this.
• Trying out different charts: Julius AI can plot your data in a myriad of ways. It is quite often an easy way for me to try things out. For example, I may test to see whether my data looks best with a violin or a box plot using the tool.

👉 If data visualisation is your main use case, check out our review of PlotStudio AI: a data-analysis tool built specifically around data analysis, plotting and creating publication-ready figures. The review also compares Julius and PlotStudio directly for academic use.

How accurate is Julius AI?

Julius AI does not directly analyse your data, it writes code (using AI) that subsequently analyses your data. The code itself is accurate. We know this because the libraries Julius AI relies on have been developed and optimised over decades. Additionally, the code is clearly visible and you can review it for accuracy.

However, the AI might not always understand what you want to do and may end up performing a slightly different task. For example, a lot of statistical tests have assumptions on the statistical properties of the data; if you don’t specify or don’t know them, the AI might assume the most probable case, possibly making a mistake. To avoid this, you can do a few things:

1. Always double-check what the AI is doing by looking at the code (it is very well-commented) and by using a good old statistics textbook if needed.
2. Make the task as narrow as possible. For example, instead of saying, “What’s the difference between group 1 and group 2?” use something more specific, “Assuming this data is normally distributed, I want to know the difference in the average and standard deviation of group 1 and group 2.”
3. If you don’t understand something, ask JuliusAI directly, as it has a chat function that will readily answer any questions.

Julius AI vs ChatGPT

Julius AI is a specialised AI tool built on a generalist model like ChatGPT, with enhanced capabilities for data analysis. Specifically, it writes code that uses your data to answer your query. If the AI makes a mistake, it can analyse the error and change the code, much like a human would. Moreover, Julius can act as an AI agent solving a problem through multiple steps rather than a single answer as ChatGPT usually would attempt to. This makes it more powerful than ChatGPT for data analysis.

On the technical side, Julius AI addresses some major limitations of ChatGPT for data analysis:

• File size: ChatGPT can only use files smaller than 512MB file size cap, while Julius can handle 8 – 32 GB files.
• Programming language: In addition to Python, Julius AI can use the R programming language, which is more familiar than Python for some and allows access to specialised packages not available in Python. ChatGPT can only use Python at the moment.
• Language model: Julius is not limited to using OpenAIs ChatGPT models but can also use Google’s Gemini and other LLMs (which can be better at coding).
• File storage: Julius offers the ability to upload your files and store them persistently, so you can have multiple chats working on the same files, while in ChatGPT, you would need to upload files every time or create a custom GPT.
• CPU/RAM management: Julius offers you the ability to increase the computing power running your statistical analysis code far beyond what is generally available to ChatGPT.

Overall, Julius is a specialised tool, great at data analysis, while ChatGPT is a general tool excelling at text, synthesis, writing, creativity and the versatility of things we all like about ChatGPT.

👉 Learn more about getting the most out of GPT-5 for research here.

Is Julius AI good for math?

Yes, Julius AI has a dedicated AI tool for solving math equations.

As you can see in the example image below: I uploaded a screenshot of an equation and asked to solve it for x. The solution isn’t elegant but it is correct. When using AI math, keep in mind that AIs don’t solve logical problems the way humans do, even if they come to correct results.

Julius AI Pricing

Julius AI is free and can be used, up to certain limits, without any cost. There are two paid plans that differ in the number of times you can use the AI per month and in the speed and performance of the models operating on your data. If, for example, your data files are very large or processing them needs more time, you may need the Pro plan to work on them efficiently. Additionally, the more expensive plan will have access to better models and larger context windows, allowing them to solve more complex problems spanning many steps.

This final feature (the ability to switch AI models) is getting more common in research tools. It’s also available in Anara AI, a research tool for literature review. Our 2026 best literature review tool roundup can get you started on how and when to use the newest AI apps for research.

👉 If you want to dig deeper in the topic of AI for academics, the latest AI Agents course takes you on a journey to build practical, efficient workflows to increase your productivity and ease of mind. The course takes a hands-on, no-fuss approach to agentic AI, focusing on building workflows that really matter for research and scholarly use.

Student Discounts for Julius AI

Julius offers 50% student and educator discounts (bringing the price to $10 to $20/month). To get these, email Julius before subscribing to get a one-time discount code.

Julius AI for Enterprise

A premium team plan ($50/user/month) is available and offers centralised billing and the longest input lengths (i.e. largest files, longest runtime). This plan is suggested for teams in large data-heavy companies.

How To Use AI for Data Analysis

As with every AI, the key is to use the right prompts and double-check the outputs. Here are my tips for successful prompting, based on my Effortless AI course.

1. Start with a very clear question – Define what you want to analyze and describe it in details
2. Few-shot prompting – For complex queries, make one or more examples to help the AI understand you.
3. Ensure clean data – Remove errors and missing values.
4. Use AI for exploratory analysis – Start shallow in your analysis and summarise datasets before running complex tests. AI is often extremely good at easier tasks but may struggle with complexity. In the next few years, this will probably become less and less true.
5. Validate with traditional methods – If publishing research, cross-check AI-generated results and talk to a statistician if you don’t understand what is happening. Statistical methods often have tricky assumptions that have to be met in order to use them.
6. Leverage AI for visualisation – A picture is worth more than 1,000 words and may give you a hint of correctness.
7. Change the prompting window often – Whenever you talk to an AI, everything in the chat is used as “context” and influences the output. When your question changes, you should flush this context by opening a new prompting window.

Summary

Julius AI offers an intuitive, coding-free way to analyse data and will probably make many academics sigh with relief. It helps with data processing, hypothesis testing, regression modelling, and visualisation. From my experience and review of the tool, Julius AI makes statistical analysis more accessible and plots more beautiful.

If you’re tired of wrestling with SPSS or R, Julius AI might be your best companion for research and coursework!