01
Friction point
Constraint 1
A regional insurer processed high volumes of FNOL submissions through manual adjuster review.
Case study
Regional Property & Casualty Insurer
Reduced claims intake processing time by 42% with governed AI automation
42% faster intake processing with maintained adjuster oversight and automated customer status updates.
42%
faster intake
Reduced
Reduced status call volume
Maintained
Maintained adjuster oversight
Client situation
Where they started
A regional insurer processed high volumes of FNOL submissions through manual adjuster review. Intake queues created 2–3 day delays on straightforward claims, increasing call center volume and customer dissatisfaction.
Client
Regional Property & Casualty Insurer
Legacy workflows
Manual handoffs
Limited visibility
Business challenge
What needed to change
02
Friction point
Constraint 2
Intake queues created 2–3 day delays on straightforward claims, increasing call center volume and customer dissatisfaction.
Approach
How Revni structured the engagement
Solution delivered
What was built
We delivered an intake orchestration layer integrating the existing claims platform, document storage, and notification services. AI-assisted classification suggested routes; adjusters approved exceptions through a lightweight review console.
Results & metrics
Business impact
Outcomes measured after delivery — not projected estimates.
42%
faster intake
Reduced
Reduced status call volume
Maintained
Maintained adjuster oversight
Before Revni
A regional insurer processed high volumes of FNOL submissions through manual adjuster review. Intake queues created 2–3 day delays on straightforward claims, increasing call center volume and customer dissatisfaction.
After delivery
Straightforward claims moved through intake 42% faster. Adjuster time shifted from repetitive triage to complex cases. Customer status updates became automated at key milestones, reducing inbound status calls.
Technology stack
PythonAWSEvent-driven workflowsDocument AIExisting claims platform APIs
Related capabilities
Capabilities applied
- AI Automation & Intelligent Systems. Practical AI automation for operations teams — workflow orchestration, document intelligence, and decision support without black-box risk.
- Digital Products & Software Engineering. Custom platforms, APIs, and product engineering with senior architecture oversight and delivery discipline.
Similar outcomes
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