AI tools typically generate a number or band based on generalized patterns. That can be useful for rough orientation, but it often misses the details that matter most in Sterling-area claims—like:
- How quickly you were treated and documented after the incident (delays can give insurers room to argue alternative causes).
- Whether your work restrictions were recorded clearly by the treating provider—especially if you worked around limitations before everything was formally set.
- How wage loss is tied to real payroll records, not just your memory of missed shifts or overtime.
- Whether your claim is moving toward MMI (maximum medical improvement) or still in an active treatment phase.
When those elements don’t line up, an AI estimate can drift low—sometimes dramatically.


