Our insurance-trained AI reads FNOL intake, documents, and legacy claims systems together, so adjusters investigate and settle claims on complete information instead of assembling it themselves.
High Claim Volumes
We triage claims by severity, complexity, and urgency, so adjusters tackle high-impact cases first.
Manual Document Review
We extract, summarize, and validate data from police reports, estimates, medical records, and photos.
Fraud Detection
We flag suspicious claims using anomaly detection, image analysis, network analytics, and fraud scoring.
Slow Claim Resolution
We automate FNOL intake, document collection, and approvals, so claims move without manual handoffs.
Litigation Risk
We flag high litigation-risk claims early, recommending proactive intervention before disputes escalate.
Data Silos
We consolidate policy, billing, claims, and documents into one system, closing gaps between legacy platforms.
Inconsistent Decisions
We surface past claims, policy language, and guidelines so settlements stay consistent across adjusters.
Customer Communication
We automate intake and allocate adjusters during CAT events, so surges don't overwhelm response capacity.
Knowledge Retention
We capture institutional knowledge and recommend next-best actions, so new adjusters ramp up faster.
Reserve Accuracy
We recommend reserve estimates and flag adjustments needed, using predictive models on historical data.
Adjuster Productivity
We generate claim notes, summaries, and correspondence drafts automatically, freeing adjusters to investigate.
The Problem
The Solution
