Recent events have caused increased interest in alternatives to American technology giants when it comes to various services, including AI. As a small hobby experiment, I wanted an AI to help me categorize my expenses based on an Excel export of my bank transactions.

First I tested providing the transaction list to various chatbots, asking them to help categorize them. I tested:

• Gemini Gem (paid version with Gemini Pro 2.0): complained a lot about data format not being right, and then it categorized things wrong most of the time after telling it what the data structure was.
• Copilot (free version): didn’t even attempt it, but explained how you can do it manually in Excel.
• Grok3: Did the task very well but could not create a downloadable CSV file with the results.
• Mistral Le Chat (free account): did the task quite well, and provided a downloadable CSV file. Some more errors in categorization than Grok3
• Deepseek: didn’t want to give them transaction data…

To test things out I then created an agent in Mistral’s La Platforme. They have a web based interface for providing instructions to the agent, quite similar to Google Gem and Microsoft’s Copilot Studio.

I asked it to categorize first income using only two categories: “Salary” or “Other income”. I gave it more categories for expenses, and told it not to mix the categories from income to expenses and vice versar. It still got confused a few times, but worked well. The agent can be deployed to both an API and the chatbot “Le Chat”. The agent expects a text string to describe the transaction in the form of a number followed by a textual description. Here’s how it works in Le Chat:

Now I want to use this in a Python script to categorize my transactions. I asked Le Chat to create a python script for me to extract transaction data into a dictionary, and then to use the La Platforme API to categorize each transaction. It first extracts the relevant data into a dataframe:

Then it iterates over the transactions and uses the agent API to ask for the category:

The result is a list of dictionaries of categorized transactions:

{‘Dato’: ‘24.02.2025’, ‘Beskrivelse’: ‘VIPPS NORSK TIPPING *8812 21.02 NOK 50.00 VIPPS NORSK TIPPING Kurs: 1.0000’, ‘Beløp’: -50.0, ‘Retning’: ‘Utgift’, ‘Kategori’: ‘Annet’}, {‘Dato’: ‘21.02.2025’, ‘Beskrivelse’: ‘REMA 1000 BREIDABLIKK *8812 20.02 NOK 97.20 REMA 1000 BREIDABLIKK Kurs: 1.0000’, ‘Beløp’: -97.2, ‘Retning’: ‘Utgift’, ‘Kategori’: ‘Dagligvarer’},

The quality of the categorization is generally good. At first I tried Mistral Nemo, a small and fast model developed with NVIDEA for categorization and simple tasks. It performed OK. Then I switched to Mistral Small 25.01, which had better performance but also a slightly different cost.

Some take-aways

• Mistral is a European company and provides high-quality AI solutions
• Their chatbot “Le Chat” and development platform “La Platforme” are both easy to use and well documented
• A plus on the privacy side: all data is processed in Europe, using an Azure datacenter in Sweden, and a new data center will be built in France. The services are covered by EU regulations such as GDPR and the AI Act.