Projects<project.case-study />
$ deploy ai-chatbot --rag --llmAI Chatbot
Enterprise AI chatbot using LLM + RAG architecture, Python REST APIs, Docker workflows, and reusable backend service design.
Problem
BFSI onboarding and support workflows need accurate, context-aware responses while reducing repetitive manual query handling.
Approach
Built around Python REST APIs, LLM orchestration, retrieval-augmented generation, reusable backend architecture, and deployment-friendly Docker/Linux workflows.
Outcome
Reduced manual query handling by 40%, improved response relevance by 30%, reduced setup time by 60%, and integrated backend services with 2+ frontend applications.
PythonREST APIsLLMRAGDockerLinuxGit
40% manual query reduction30% response relevance improvement60% setup time reductionIntegrated with 2+ frontend applicationsPython REST APIsReusable backend service design
Architecture notes
Interface screenshot
Outcome metrics