Production AI Systems for Enterprise Clients

The retailer's customer service team handled 300,000+ monthly inquiries across chat, email, and mobile app — order status, returns, product questions, and store information. Response times averaged 8+ hours, CSAT scores were declining, and scaling the human team was cost-prohibitive during peak seasons.

We built a multi-LLM AI customer experience platform with intelligent routing. The system uses Google Gemini for conversational AI, OpenAI for complex reasoning tasks, and a RAG pipeline over product catalogs, policies, and order data. An AI agent orchestration layer handles multi-step workflows (refunds, exchanges, escalations) with full autonomy for routine cases and seamless human handoff for complex ones.

• Multi-LLM orchestration with cost-aware model routing
• AI agent workflows for order management, returns, and escalation
• RAG over 150K+ product catalog and 2,000+ policy documents
• Real-time order system integration via function calling
• iOS and Android customer app with embedded AI chat
• Agent supervisor with guardrails and sentiment detection
• Analytics dashboard with CSAT prediction and conversation insights

• Google Gemini
• OpenAI
• LangGraph
• AI Agents
• RAG
• FastAPI
• Swift / iOS
• Kotlin / Android
• AWS Bedrock
• PostgreSQL
• Redis

Medical affairs and clinical research teams needed to query across 500,000+ clinical documents — trial protocols, adverse event reports, regulatory submissions, and research papers. Finding relevant information took researchers hours of manual searching, and answers often missed critical cross-references between studies.

We built a production RAG platform specifically designed for clinical and pharmaceutical knowledge retrieval. The system uses advanced chunking strategies for scientific documents, hybrid search (dense vectors + BM25), multi-step reasoning chains for complex clinical queries, and strict citation tracking to maintain scientific rigor. All outputs include traceable references to source documents with page-level citations.

• Production RAG pipeline over 500K+ clinical documents
• Hybrid retrieval: dense embedding vectors + sparse BM25 search
• Domain-specific chunking for clinical trial protocols and papers
• Multi-step reasoning with chain-of-thought for complex queries
• Citation tracking with page-level source references
• Role-based access control (HIPAA-compliant architecture)
• Evaluation pipeline with domain-expert feedback loops
• Web application with conversation history and saved queries

• OpenAI
• Azure AI
• RAG
• pgvector
• LangChain
• FastAPI
• Python
• Azure Cloud
• Hybrid Search
• LangSmith
• RAGAS