Context
Insurance operations depend on accurate reading of clinical and financial documents under time pressure. Legacy processes mixed manual review with brittle rules.
Business problem
Operators spent disproportionate time categorizing documents, reconciling fields, and applying policy rules—slowing claims and pre-authorisation cycles.
User or operational pain
Inconsistent extraction quality created rework. Supervisors lacked confidence in when to trust automation versus escalate.
Product intervention
A cognitive automation layer on the core workflow platform: ingestion, categorization, field extraction, rule evaluation, and operator decision support—with explicit confidence scores and override paths.
AI capability used
Computer vision and NLP for document understanding, structured extraction, rule-based decision support with ML-assisted signals, and decision UI embedded in operator workflows.
Samadhan's role
Samadhan led product strategy for an AI-powered decision-support platform: operator journeys, quality bars, eval design, HITL policy, and phased rollout with engineering and operations.
Business outcome
Reduced manual handling on high-volume document types, faster routing to the right expert, and improved traceability for compliance review.
Lessons for other companies
Operator trust is a product requirement. Publish confidence, show sources, and design overrides as first-class—not afterthoughts.
Related service: AI Agents Consulting · Insights
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