AI Assisted Maintenance Troubleshooting
Designing an AI conversation flow that helps residents troubleshoot maintenance issues themselves
Product UI is proprietary. Diagrams represent the design thinking, not the actual interface.
Designing the conversation, not the screen
How AI-guided follow-ups and troubleshooting replace manual back-and-forth. The conversation flow is what I designed and shipped.
no priority signal
PMC gets a record, no action needed.
PMC gets richer data upfront.
fewer back-and-forth cycles
Originally shelved by the web team. Reframed as a mobile-first initiative and led it after recognizing the opportunity.
Multi-picker for exploratory follow-ups (gathering breadth). Single-select for troubleshooting steps (constrained action). The input type changes because the cognitive task changes.
Residents can skip any AI question or bail out entirely. The AI watches for discomfort signals and respects them. No dead ends.
PMCs confirmed the AI asks exactly the questions they would ask manually, which meant the conversation logic was ready for real tickets
The web design team started using the same working-prototype approach to test their own AI features
The system prompt I wrote during prototyping was used as engineering's production starting point
Why the interaction pattern changes at every step
This looks like a chat interface, but the underlying flow is structured. Each interaction pattern was chosen to match what the resident is trying to do at that point in the conversation.
System prompt generates contextual follow-ups The AI generates contextual follow-ups based on the maintenance description. Multi-picker lets residents select multiple applicable answers, which fits exploratory questions where breadth matters more than precision.
Decision tree narrows with each response When the AI has enough context, it provides the most likely troubleshooting step. Single-select constrains the response — the resident either confirms success, reports failure, or skips. Each response narrows the AI's decision tree.
Always-on monitoring layer The AI monitors every response for emergency signals — gas leaks, flooding, electrical hazards, anything involving immediate risk. It runs throughout the entire interaction, on every message.