Most AI tools generate a range based on simplified inputs (injury severity, age, treatment type, and similar factors). They can’t see the details that typically drive value in real cases—like imaging findings, neurological exams, wound/skin risk, respiratory complications, or how quickly a person reached stabilization.
In Orinda, where many incidents involve commuter traffic, hillside roads, and residential intersections, liability evidence often turns on practical questions:
- What did witnesses observe immediately after the crash?
- Were there traffic-control issues or sightline problems?
- Did the incident happen in a context that affects comparative fault?
- Are there timely records from EMS, hospitals, and follow-up specialists?
An AI estimate won’t weigh those facts. A legal team does.


