Enterprise RAG: AI-Powered Knowledge Base

Project Details

• Managed ByOctolade
• Handover

Services

Overview

Core Objective: Eliminate internal information silos by creating a Retrieval-Augmented Generation (RAG) chatbot that provides instant, accurate answers from company-wide documentation. Tech Stack: Orchestrated via Google Gemini (Pro & Flash) for reasoning, Pinecone for high-performance vector storage, and Google Drive for seamless document synchronization. Scope: A fully automated Brain for the organization that indexes PDFs, Docs, and Sheets in real time to support HR, Legal, and Technical Support teams.

The Challenge

Document Drift: Company policies and project specs change daily; manual knowledge bases quickly become obsolete and provide outdated information. Information Overload: Employees spend significant time searching across cluttered Drive folders. Contextual Accuracy: Standard AI models hallucinate on company-specific questions due to lack of access to private data.

My Solution

We architected a sophisticated RAG pipeline that transforms Google Drive into a dynamic vector database. A dual-trigger workflow detects new or modified files, processes them with a Recursive Character Text Splitter, and generates embeddings using Google text-embedding-004. When an employee asks a question, an AI Agent queries Pinecone to retrieve top chunks which Gemini Pro synthesizes into concise, factual answers. With Window Buffer Memory, the bot maintains conversational context for deep policy exploration.

Results

The system reached 98% accuracy in retrieving internal policy details and reduced 'Where can I find...?' inquiries by 65%. New uploads become searchable within seconds, streamlining onboarding and internal support. Employees save 4+ hours per week on manual research, aligning the organization around a unified source of truth.