AI Policy Assistant

Project Overview

#

I engineered a Retrieval-Augmented Generation (RAG) system designed to bridge the gap between static corporate documentation and interactive AI. The system autonomously monitors internal directories, processes unstructured data, and provides a conversational interface for complex policy queries.

1. Automated ETL Pipeline (Data Sync)

#

Instead of manual data entry, I built an automated ingestion layer that keeps the AI’s “brain” current:

• Source Integration: Monitors Google Drive repositories for new or updated policy documentation.
• Vectorization: Utilizes Google Gemini Embeddings to convert text into high-dimensional vectors.
• Database Management: Automates the flushing and re-indexing of a Pinecone Vector Database to ensure 100% data consistency.

2. Intelligent Document Processing

#

To ensure the AI handles large documents accurately, the engine employs sophisticated text-handling logic:

• Recursive Chunking: Breaks down large PDF/Docx files into optimized segments with a 100-character overlap to maintain semantic context across chunks.
• Metadata Tagging: Ensures every piece of retrieved information is traceable back to its source.

3. Agentic Logic & Retrieval

#

The front-end interface utilizes an “Agentic” model rather than a simple prompt:

• Contextual Memory: Implemented a Window Buffer Memory system, allowing the AI to remember the last several turns of a conversation for fluid follow-up questions.
• Semantic Search Tooling: The agent is equipped with a custom-tooled vector retriever, allowing it to perform “lookups” in real-time before formulating a response.
• Zero-Shot Guardrails: Engineered strict system prompts to prevent the AI from speculating; if the answer isn’t in the verified docs, it will not provide a false answer.

Technical Architecture Summary

#