Solution Feature
AI notebooks offer an integrated working environment that makes it easier for teams to evaluate stored machine data, formulate questions by voice and prepare results directly as visualizations, tables or calculations.
Request a demoAI notebooks in everyday life
AI Notebooks are designed for the evaluation of already stored machine data. They combine an interactive working environment with language support that generates Python code for the Bytefabrik Python client based on natural input.
This means that even users without in-depth programming knowledge can start more sophisticated analyses. The generated code loads the data securely from the platform instead of sending the actual data to the language model. This also enables production, quality and process managers to achieve reliable results more quickly.
The code remains fully visible and can be explained, adapted, re-executed and shared with other users. This makes analyses understandable, comprehensible and reusable within the team.
Typical questions
AI notebooks are particularly helpful when teams want to quickly examine specific production issues in a structured way.
Typical examples are Why is scrap increasing for a particular product variant? Which parameters change before quality problems occur? How do shifts, lines or batches differ over time?
Code generation
A language model generates Python code for the Bytefabrik Python client, which is used to securely load and analyze stored platform data. The code remains visible, can be explained and helps teams to implement even more complex analyses more quickly.
Working environment
AI Notebooks automatically generate suitable visualizations, tables and structured results from the analysis code. In addition, form fields and parameter inputs can be integrated to develop reusable notebooks that can be shared with other users.
From a quick start to reusable analysis
AI Notebooks access platform data that has already been saved and make complex evaluations possible without having to manually search for data sources.
Non-programmers can have analysis code generated by language and thus initiate visualizations, tables and calculations themselves.
The generated code remains visible, can be explained, adapted and later re-executed or further developed.
The language model generates code for the Python client; the actual platform data is not sent directly to the model.
Notebooks can be shared with other users and, if required, expanded to include form fields and parameter entries for reusable evaluations.
Demo
Arrange a demo to get to know the platform, analysis and AI functions based on your questions.