Case Study: Epic Homes RAG Implementation

Overview

Epic Homes, a prominent UK real estate firm, sought to streamline their lead generation process by leveraging AI to automate repetitive tasks and provide accurate information quickly.

The Challenge

• Sales representatives spent hours daily answering frequently asked questions (FAQs) about properties, mortgages, and financing.
• Manual searching through over 1000 documents was time-consuming and inefficient.
• Repetitive queries led to inconsistencies in responses.
• Lack of a centralized knowledge base made it difficult for new hires to quickly become effective.

The Solution

1. Data Ingestion: Collected and ingested 718 transcripts and documentation related to properties, mortgages, and financing into the PYROX AI system.
2. Vectorization: Converted the ingested data into 56k vectors using Gemini embeddings for efficient similarity search in Qdrant.
3. Knowledge Base Setup: Deployed a RAG (Retrieval-Augmented Generation) knowledge system on a dedicated Mikrus instance to manage and retrieve information.
4. API Development: Developed a FastAPI-based rag-service that interfaces with the Qdrant vector store, enabling quick and accurate responses.
5. Integration with Telegram: Created a Telegram bot for sales reps to interact with the AI system easily from their mobile devices or computers.
6. Continuous Improvement: Established a feedback loop to refine the knowledge base based on user interactions and new document additions.

Results

• Automated 80% of FAQs, significantly reducing the time spent on repetitive tasks.
• Saved approximately 4 hours per day for each sales representative.
• Ensured all answers were source-cited, eliminating potential inconsistencies or "hallucinations."
• Improved customer satisfaction by providing immediate and accurate information.

Tech Stack

• Qdrant
• Gemini embeddings
• FastAPI
• Telegram Bot
• ONNX runtime

What's Next

Epic Homes plans to expand the AI system’s capabilities by integrating it with their CRM for personalized outreach, enhancing predictive analytics for lead scoring, and exploring natural language processing (NLP) features for more advanced conversational interactions.

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