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To enable a large Indian digital financial services organization to scale financial product discovery and advisory, reduce support costs, improve conversion rates, and deliver compliant, personalized financial guidance using a Generative AI–driven conversational platform.
The client is a leading Indian digital financial services enterprise operating at national scale, offering a broad portfolio of financial products including digital banking, lending, insurance, credit cards, and investment solutions.
With a rapidly growing, mobile-first user base across Tier 1–3 cities, the organization aimed to deliver high-quality financial guidance at scale while maintaining regulatory compliance, data security, and operational efficiency.Traditional advisory channels such as call centers and branch-based support were not viable for serving millions of digitally native users cost-effectively.
BootLabs partnered with the client as their Generative AI and Google Cloud engineering partner to design and implement an enterprise-grade conversational AI platform.
The engagement focused on:
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Users struggled to navigate and compare a large number of financial products across lending, insurance, cards, and investments without a unified advisory experience.
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Manual advisory models could not economically support a massive digital user base, resulting in high support costs and limited scalability.
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High-traffic events such as vehicle launches required infrastructure that could scale reliably without performance degradation.
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Rapid cloud adoption increased the need for stronger financial governance to reduce wastage and improve cost efficiency.
Designed an agentic AI platform using Google ADK, enabling specialized domain agents for loans, insurance, credit cards, deposits, investments, and customer support.
Implemented a confidence-driven routing framework using intent confidence, capability confidence, and engagement risk to ensure reliable, auditable decision-making.
Built domain-specific Vertex AI RAG corpora to enable accurate, citation-backed responses from structured and unstructured financial documentation.
Integrated Model Armor, DLP, and output guardrails to prevent prompt injection, protect sensitive data, and enforce compliant response behaviour.
Deployed on Google Kubernetes Engine with auto-scaling, observability, and high availability to support peak traffic and future growth.
Reduced average customer query resolution time from several minutes to under 90 seconds.
Automated the majority of Tier-1 financial queries, significantly lowering operational support load.
Conversational guidance led to a substantial increase in financial product exploration sessions.
Personalized recommendations and eligibility guidance more than doubled conversion across select product categories.
Achieved over 99.9% uptime with zero production rollbacks.
Achieved over 99.9% uptime with zero production rollbacks.
Users received instant, easy-to-understand financial guidance available 24×7 across devices and geographies.
Automation reduced reliance on human advisors, allowing teams to focus on complex, highvalue customer interactions.
Conversation analytics provided actionable insights into customer intent, product gaps, and cross-sell opportunities.
The platform established a safe, auditable foundation for AI-driven financial advisory in a regulated environment.
The architecture supports additional AI use cases such as voice advisory, vernacular language support, and transaction enablement.
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