Many AI estimators are built on generalized patterns. They may ask you to select an injury level or severity and then generate a range for damages.
In real Mebane cases, however, insurers frequently focus on details that AI tools can’t fully capture, such as:
- Whether early symptoms and hospital documentation align with the claimed onset
- How functional limitations were measured (not just the diagnosis label)
- Whether complications emerged (for example, skin breakdown risk, respiratory issues, or mobility decline)
- How consistent the care timeline is with the expected recovery trajectory
When the evidence doesn’t line up, the “average” payout model can drift far from what’s actually defensible.


