AI tools typically work by taking inputs (injury severity, treatment length, missed work) and producing a broad range. That can reduce uncertainty in the early days.
But many Culver City cases turn on details that generic tools can’t reliably “see,” such as:
- Stop-and-go congestion that affects how quickly a truck could slow and whether braking systems were functioning properly
- Lane-change and merge dynamics common on major routes, where video and event-data can matter more than the “headline” of the crash
- Pedestrian and cyclist proximity to the roadway in busy commercial stretches, which can expand injury documentation needs and medical timelines
- Multiple responsible parties (driver + employer + maintenance vendors) that often require records beyond what an AI questionnaire captures
In short: an AI number may mirror “average” cases, but your settlement value depends on what can be proven about fault and causation.


