FixNavi: AI-assisted repair knowledge for devices

Start with the actual device

Rather than searching for a generic fault alone, the workflow starts with a specific device. Users enter its model number or photograph its rating plate. Combined with a description of the problem, this provides the context for more focused research.

The photo is an input method, not a physical inspection. The identified model needs to be correct before information about possible causes or parts can be assessed meaningfully.

Turn research into reusable repair knowledge

FixNavi brings together device details, symptoms and researched information to help users assess possible repair routes and replacement parts. Suitable cases contribute to a public, anonymised knowledge base with model context and sources.

The project connects an individual question with knowledge that can be found again. A repair case can therefore serve more than one enquiry and help other people facing a similar problem.

Support a decision without replacing professional inspection

Finding a spare part does not prove that it fits. The device variant, part number and source still need to match the actual situation. An AI-assisted assessment likewise cannot replace measurements or a professional inspection of the device.

Its value is in orientation: bringing information together, making unresolved questions visible and preparing the next step. Depending on the fault, that may mean a manageable repair, professional assistance or deciding not to repair.

What the project demonstrates

FixNavi combines image and text inputs with a practical use case and a structured knowledge base. For ProWebSolutions, it demonstrates how AI capabilities become part of a usable web application: from a simple starting point through research to a clear presentation of findings and their limitations.