I build custom AI assistants and RAG chatbots grounded in company documents, product data, internal knowledge, and live business systems.
The assistant can answer questions, search business information, guide users through workflows, collect structured details, retrieve current data through APIs, and transfer complex conversations to a human operator.
Services include:
• RAG architecture and implementation
• Document ingestion, chunking, indexing, and retrieval
• Qdrant and other vector database integrations
• Stateful conversation flows with LangGraph
• Python and FastAPI backend development
• LLM integration through LiteLLM and compatible providers
• Streaming responses with Server-Sent Events
• REST API and RabbitMQ integrations
• Conversation memory and session management
• Intent routing and structured data collection
• Prompt engineering and response validation
• Guardrails and restricted knowledge boundaries
• Human handoff workflows
• Knowledge-gap and feedback collection
• Integration with websites, mobile applications, and backend systems
• Docker deployment, logging, and automated testing
I have implemented a production AI assistant for a real estate marketplace using FastAPI, LangGraph, Qdrant, LiteLLM, PostgreSQL, RabbitMQ, and a Spring Boot backend.
Please send the intended use case, available knowledge sources, required integrations, expected conversation flow, and deployment environment. I will review the scope and propose an implementation plan.