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Artificial Intelligence / Enterprise AutomationAI & Automation

AI / RAG Enterprise Automation System

Knowledge Search & AI-Powered Workflow Engine

Role: AI & Automation Lead Developer
Period: 2025 – Present
Project Links & Resources

Project Overview & Architecture

An enterprise AI integration system built to solve document lookup friction across thousands of complex travel policies, terms, and internal operational rules.

Utilizing RAG (Retrieval-Augmented Generation), vector embedding search, and LLMs, the platform allows internal staff and agents to query operational knowledge bases in plain natural language.

Features automated email drafting for customer service inquiries, document summarization, and automated background tasks for invoice data extraction.

Key Platform Features

Natural language document search over complex travel rule PDF/Doc files
Context-aware AI assistance with exact source citations
Automated email response drafting based on incoming customer queries
Background worker pipeline for automated document vectorization
Role-gated AI query access to ensure sensitive data protection

Engineering Challenges & Solutions

Challenge: Preventing hallucination when retrieving technical travel refund guidelines and penalties.
Solution: Implemented strict chunking, hybrid vector/keyword search, and prompt guardrails enforcing exact document citation requirements.

Impact & Key Metrics

Search Accuracy94%
Support Time Saved50%

Technologies Used

PythonTypeScriptNode.jsVector DB / EmbeddingsLLM APIs (OpenAI/Anthropic)React.jsTailwind CSS