CASE STUDY

AI Upgrade Cuts Support Load and Improves Response Accuracy for Healthcare Chatbot

Healthcare Benefits Administration

1,000 - 2,000 employees

USA

A leading healthcare benefits administrator serving Fortune 500 companies faced significant limitations with their Amazon Lex-based chatbot. Users experienced frustration with the rigid, keyword-based system that lacked contextual awareness and intelligent knowledge retrieval capabilities.

Deploy.AI enhanced the existing chatbot with large language model capabilities and advanced Retrieval-Augmented Generation, transforming it into a conversational assistant that accurately answers complex benefits questions while maintaining compliance requirements.

The Client

The client, a top-tier healthcare benefits administrator, provides employee benefits management for Fortune 500 companies.They support over a million users annually, offering tools to help employees make informed healthcare decisions.

Reached 60% fewer escalations to human agents

Dramatically improved user satisfaction and trust through citation-based responses

Improved sub-second response latency

In healthcare administration, up to 30% of support interactions involve benefit clarifications — often during limited open enrollment periods. Delays or misinformation risk compliance and user frustration.

The Challenge

The client’s legacy chatbot — based on Amazon Lex — was showing its age:

Keyword-based processing limited natural conversation handling
No memory of prior messages, leading to repetitive, fragmented interactions
Static API calls lacked the intelligence to tailor responses to complex queries

This created operational inefficiencies, high support ticket volumes, and luck of trust in automated responses.

The Solution

Rather than replacing Lex, Deploy.AI introduced a custom-built AI application that enhanced its intelligence, contextual awareness, and responsiveness.

Advanced RAG with Vector Database

Pulled real-time, contextually relevant content from the client’s knowledge base.

Two-Tiered Answer Logic

Simple queries got concise answers; complex ones triggered long-form, compliant responses.

LLM Context Retention

Chat sessions retained memory, enabling conversations, replacing standard Q&A chat.

JSON Validation and Code Layer

All responses structured in schema-validated JSON, seamlessly integrating into existing systems.

Citation-Based Responses for Accuracy and Compliance

Each answer linked directly to source material, which is critical in regulated industries.

Enhanced User Experience with Follow-Up Questions

Auto-generated button-based follow-ups helped users navigate benefits they might rarely interact with.

Reduced Support Load and Enabled Accurate, Scalable Responses

Our collaboration with the client provided fruitful results:

The AI-enhanced chatbot now delivers dynamic, context-aware answers — reducing the need for human intervention.
Built-in compliance and source citations have increased user trust and response reliability.
The system is scalable and ready to support additional benefits categories without major rework.
Ongoing model evaluation ensures that AI performance continues to improve automatically.
Integration with existing reduced onboarding time and cost, enabling faster rollout of new services.