Ziezodan makes repair intake smarter with AI: one report with text and photo
Ziezodan wanted to make repair requests more user-friendly. We designed an AI intake that lets tenants report issues in their own words (and optionally with a photo).
- Industry
- Property maintenance & proptech
- Headquarters
- Hengelo, Netherlands
- Founded
- 2017
- Solution
- AI intake agent
The challenge
Ziezodan has a strong structure for repair reports via a decision tree. The catch: for tenants it feels like a lot of clicking, and the real context (description and photo) only comes right at the end. As a result, reports tend to be generic and less useful for follow-up.
The solution
We designed an AI-driven intake that supplements or partly replaces the decision tree. The tenant starts straight away with a short description and can optionally add a photo. Then:
AI extracts the room, element and problem from the text (NLP)
The photo helps to confirm or complete this (image recognition)
If something is missing, the AI asks targeted follow-up questions (“Which room is this in?”)
The report is mapped to the exact JSON values from the existing structure, and only then forwarded
What it delivers
Faster, more natural reporting for tenants
More consistent reports with more context
Data that plugs directly into the existing JSON hierarchy, with no changes to the downstream process
Fewer clicks
Report faster
- Context first
- Text and photo
- 1 overview
- Better data entry
