Claimant Coach
This variant isolates the claimant coaching moment from the broader FNOL wizard. A pre-extracted packet is checked for delay, appeal, and denial-driving gaps. The small coaching model predicts fixed catalog gap IDs, and every claimant-facing recommendation comes from carrier-approved language rather than free-form generation. It is designed for carriers evaluating whether a mobile app can help customers submit cleaner evidence without creating hallucinated policy advice.
The Problem
Claimants often submit incomplete packets because they do not know which details will trigger manual review. Static forms and rule checklists catch obvious missing fields but miss narrative ambiguity. Cloud LLM coaching can sound helpful, but it creates unacceptable policy-language and data-residency risk.
How LFM Compares
Most carriers push the burden to adjusters, call centers, or post-submission document requests. That creates avoidable stalls, repeat contacts, and appeals after the claimant believes they have already done the work.
What LFM Unlocks
A small on-device classifier can run before submission, identify known gray-zone gaps, and route each gap to a carrier-approved coaching response. The claimant receives specific next-best actions while the insurer keeps legal language constrained and auditable.
CLAIMANT-FACING Submission Coach
Before filing, the app tells claimants which gaps will stall the packet, then captures the fix with carrier-approved language.
Choose packet
Both presets reuse the FNOL evidence and extracted fields from the full demo, then start at the moment a claimant is about to submit.
This demo is fine-tuned on sample data. Results improve with your data.