AI tools typically respond to inputs like bite location on the body, treatment duration, and whether surgery was required. Those factors matter—but they don’t capture the details that drive outcomes in real Walnut claims.
For example, local cases often hinge on questions like:
- Where the bite happened (front yard vs. inside a home vs. common areas shared by residents)
- How quickly the injury was documented (photos, wound descriptions, prompt medical care)
- Whether the dog showed prior aggressive behavior known to the owner
- How the incident was described to medical providers and how consistent that record is
- Whether there were witnesses who saw the dog’s behavior and the moments leading up to the bite
When those pieces are missing or unclear, an AI estimate may skew low or high—because it’s using generalized patterns rather than the specific proof your claim can support.


