AI Support Chatbot with RAG
A knowledge-grounded support chatbot using Retrieval-Augmented Generation to answer user inquiries accurately and capture high-intent leads.
Client
AI Chatbots
Deployment Category
AI Chatbots
Engineering Duration
10-15 Days
Tech Stack
OpenAI, n8n, Supabase Vector DB
Manual Bottlenecks
Operational inefficiencies, typing backlogs, and slow response loops led to missed sales conversions.
Automated Solution
Deploy secure database integration nodes, sync catalog structures, and execute automatic customer updates.
Execution Blueprint & Details
This case study demonstrates how modern language models can be securely grounded in custom company knowledge bases without the risk of hallucination. By using Retrieval-Augmented Generation (RAG), the bot only answers questions with facts present in the uploaded company guide files.
Technical Implementation
First, we built an ingestion workflow that splits long documentation PDFs and text files into semantic paragraphs. Each paragraph is turned into a 1536-dimensional vector using OpenAI's text-embedding-3-small model and stored in Supabase via the pgvector extension.
When a visitor types a question in the chat widget, we query Supabase to find the top 3 most relevant documentation chunks. These chunks are injected into the LLM system prompt, instructing it to answer strictly based on the provided text. If the answer cannot be found in the chunks, the bot politely asks the user to fill out a quick form which triggers an instant email/Slack ping to human support agents.
Proven Metrics Achieved
Ticket volume decreased by 55% within 3 weeks, and the bot captured 80+ qualified sales leads through the intelligent fallback system.
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