At a Glance

40% call volume reduction | <10 seconds response time | 90% accuracy rate | ₹2.4-8 crore annual savings

ICICI Lombard transformed customer support from 8-12 minute phone queues to instant AI-powered assistance, achieving 85% user satisfaction while dramatically reducing operational costs.

The Challenge

Healthcare insurance policies are notoriously complex, with 50-plus page documents filled with medical terminology and legal jargon. For ICICI Lombard, this complexity created friction at every customer touchpoint.

Customers endured unbearable wait times of 8-12 minutes for simple questions like whether maternity is covered or which hospitals are in their network. Support agents consumed 60-70% of their time answering repetitive queries, preventing them from handling complex cases requiring human expertise.

Policy document complexity made self-service nearly impossible. Static FAQ systems achieved less than 20% self-service resolution rates, failing to address nuanced insurance questions. Inconsistent responses damaged trust—different agents interpreted policy language differently, creating a 30% decision inconsistency rate. Scalability pressures compounded challenges as support costs scaled linearly with volume growth, while peak periods created dramatic wait time spikes.

The Solution: AI-Powered Insurance Assistant

DevKraft deployed a production-ready AI insurance assistant in just 8 weeks. The solution employs advanced document intelligence powered by LangChain, Qdrant vector database, and OpenAI embeddings, achieving 90% accuracy compared to traditional keyword search's 40-50% rate.

The multi-strategy document processing uses healthcare-optimized chunking that eliminates AI hallucinations by grounding responses in actual policy documents. Intelligent query translation converts questions into 3-5 optimized search variants, dramatically improving relevance. UIN-based filtering ensures responses match each customer's specific policy.

Intelligent multi-LLM routing automatically selects between OpenAI GPT-4.1 for complex queries, GPT-4.1-mini for lightweight tasks, and Gemini 2.0 Flash for session management. This balances cost and quality—using efficient models for simple queries while leveraging GPT-4's reasoning for complex interpretations.

The high-performance backend runs on FastAPI with Python 3.12, utilizing Qdrant vector database and AWS S3. FastAPI's async architecture delivers response times under 10 seconds even under load.

Voice intelligence through ElevenLabs supports multilingual conversations in English, Hindi, and regional languages with natural Hinglish conversational style. Mobile-based authentication uses last-4-digit matching, allowing policy access without requiring policy numbers.

Implementation Journey

Phase 1 (Weeks 1-3) established the policy document ingestion pipeline and integrated OpenAI APIs, delivering a functional AI backend with 30+ policy documents indexed.

Phase 2 (Weeks 4-6) developed multi-channel interfaces supporting text and voice, added ElevenLabs integration, and built intelligent query translation generating 3-5 optimized searches per question.

Phase 3 (Weeks 7-8) conducted pilot deployment, optimized performance, and completed safety testing, producing a production-validated system achieving 85% user satisfaction.

Transformative Business Impact

Call volume reduction reached 40%, with average response time dropping from 8-12 minutes to under 10 seconds. The 90% accuracy rate generated zero escalations during the pilot phase.

User satisfaction reached 85%, who particularly valued 24/7 availability. Self-service adoption increased 2.5x, empowering customers to find answers independently. Query resolution transformed to consistent 90% accuracy. Self-service rates improved from under 20% to over 50%. Cost per query decreased by 90%, from ₹500 to ₹50.

Cost savings scale dramatically. At pilot scale with 20,000 monthly calls, annual savings reach ₹1.08 crore. Full scale at 80,000 calls generates ₹4.51 crore annually, while enterprise scale at 120,000-150,000 calls produces ₹6.72-8.52 crore. This occurs because AI costs grow logarithmically while call center costs scale linearly.

Support agents now focus on complex, high-value interactions. The scalable architecture handles demand surges without adding headcount. IRDAI-compliant responses with audit logging maintain regulatory compliance. Multilingual accessibility expands reach across India.

Key Innovations

Multi-strategy document intelligence combines semantic search with intelligent query translation, achieving 90% relevance compared to 70% for single-strategy approaches. Voice-first design through ElevenLabs integration with Hinglish conversational style increases accessibility, particularly valuable during stressful medical situations. Mobile-based authentication eliminates friction while maintaining security.