AI tools typically work by asking questions about injuries and losses, then assigning ranges based on generalized patterns. That can help you ballpark categories like medical bills and wage loss.
In Salisbury, though, the missing piece is often how liability is actually established. For example:
- Chain-of-custody evidence: In many crashes, key proof (truck maintenance history, driver qualification records, post-crash inspection notes) sits with the carrier and takes time to obtain.
- Causation disputes: Insurers may argue your symptoms are unrelated, especially if you had prior treatment, pre-existing conditions, or a gap in care.
- Comparative fault arguments: Even if you didn’t drive the commercial vehicle, insurers may claim you contributed—like improper lane positioning, sudden braking, or pedestrian/vehicle conduct near busier corridors.
An AI estimate can’t verify which defense the insurer is likely to raise in your specific Salisbury scenario, or how persuasive your medical timeline will be.


