The first line of insurance support, structured for resolution.
A specialist health insurer faced repetitive service demand across policy information, claims, renewals, and FAQs. Mantra Labs built a chatbot-led service layer with workflow routing, channel extension, escalation, and reporting.
Insurance support automation only works when it guides customers to the right next step.
The opportunity was not to add a generic bot widget. It was to create a useful first layer of service for common customer intent.
Automation had to preserve escalation when the interaction required judgment, exception handling, or reassurance.
High-volume repetition
Policy, claim, renewal, and FAQ queries consumed support capacity.
Complex web journeys
Customers could drop off while searching dense web pages for simple answers.
Consistency pressure
Regulated support contexts need reliable tone, completeness, and next-step clarity.
Cost-to-serve pressure
Routine queries needed a scalable handling layer before agent escalation.
A guided conversational layer for common insurance service journeys.
The system combined customer-facing conversation flows, configurable workflows, intent recognition, backend integration, WhatsApp extension, live-agent escalation, and reporting.
Customer-facing conversational UI
Guided customers through common policy, claim, renewal, and FAQ paths.
Configurable workflows
Allowed service teams to extend and manage common flows and sub-flows.
Workflow integration
Moved selected interactions beyond static FAQ responses into practical service paths.
Live-agent and reporting layer
Supported escalation, fallback visibility, usage patterns, and ongoing optimization.
From customer question to resolved or escalated service need.
Ask
The customer starts with a policy, claim, renewal, or support question.
Clarify
The bot asks structured follow-ups instead of sending users into generic pages.
Resolve
Common actions and answers are handled through configured service flows.
Escalate
Live support takes over when automation is not enough.
Learn
Reporting surfaces flow performance, fallback behavior, and repeated demand patterns.
Conversational automation works when it reduces uncertainty, not when it traps users in generic answers.
What changed when the workflow became connected.
Customers relied heavily on web pages or assisted support.
Customers could begin with guided conversational service.
Repetitive queries consumed agent capacity.
Common queries moved into automated workflows with escalation paths.
Responses could vary across assisted channels.
Standardized flows improved consistency across frequent questions.
Support teams had limited visibility into repeated interaction patterns.
Reporting surfaced handled flows, fallback behavior, and usage patterns.
The hard part was designing conversation around real insurance-service intent.
Intent mattered
The bot needed to understand service needs quickly, not simply match keywords.
Resolution had to be practical
Customers needed answers and next actions, not another web-navigation layer.
Fallbacks were part of the product
Escalation had to be clean when automation could not resolve the interaction.
Reporting made improvement possible
Fallback and usage patterns needed to feed service optimization.
Mantra can automate repetitive support without weakening the customer relationship.
Automation with escalation
The solution kept human support available where judgment or reassurance was required.
Channel extensibility
Conversation logic could support web and selected messaging-channel use cases.
Service coverage
Common policy, claim, renewal, and FAQ intents became guided paths.
Optimization visibility
Reporting gave service teams a better view of customer demand patterns.
The capabilities behind the build.
AI & Automation
Conversation logic and workflow automation absorbed high-frequency support demand.
Experience Transformation
Customers gained a more guided route to answers and service actions.
Governance & Compliance Enablement
Standardized flows improved consistency in a regulated support context.
Digital Product Engineering
Integration and escalation layers turned the bot into a service system, not a widget.
Other systems where complexity had to move.
Everyday health-insurance service, closer to the customer.
A mobile-first customer engagement app for health check-ups, claims, OPD access, reports, renewals, and policy self-service.
A health insurance mobile app for check-ups, OPD access, claims intimation, reports, renewals, and policy service journeys.
Insurance decisions made clearer on mobile.
A mobile-first insurance engagement platform designed around customer decisions rather than insurer departments.
Product discovery, buying, claims, renewals, and value-added services redesigned around clearer customer decisions.
Claims documents, read and routed before review begins.
An AI-assisted claims automation system for document ingestion, OCR, line-item extraction, benefit bucketing, and reviewer validation.
Claims documents processed through OCR, NLP, line-item extraction, benefit bucketing, and reviewer workflows to reduce manual adjudication load.