AI Support Bot with LangChain
I replaced Intercom ($2K/mo) with a custom AI support bot built using LangChain + OpenAI. Handles 85% of tickets automatically. Total cost: $47/mo in API calls.
Replaced a $2K/mo support tool with AI
Revenue Impact
$1,953/mo savings ($2,000 - $47)
Real Results
Tickets resolved without human intervention
Down from $2,000/mo on Intercom
Down from 4 hours average
Step-by-Step Guide
Document your knowledge base
Export all support articles, FAQs, and past ticket resolutions. Format as markdown files organized by topic.
Pro tip: Use ChatGPT to reformat your knowledge base into clean Q&A pairs for RAG ingestion.
Build a RAG pipeline with LangChain
Ingest your knowledge base into a vector database (Chroma or Pinecone). Set up a LangChain retrieval chain that searches relevant docs for each query.
Add a confidence threshold
Set a confidence score cutoff. Below 85% confidence, route to human support. This prevents the AI from guessing.
Connect to Telegram for instant response
Set up a Telegram bot that forwards messages to your RAG pipeline. Response time: 2 seconds vs 4 hours average.
Pro tip: Add "escalate to human" as a button on every AI response. Users need the safety net.
Monitor and improve weekly
Review the 15% of tickets the AI couldn't handle. Add those to your knowledge base. The resolution rate increases by ~5% weekly.
Sample Prompts & Results
AI retrieved the relevant KB article about OAuth configuration, identified the missing redirect URI, and provided step-by-step fix instructions.
The RAG pipeline correctly matched the error message to the solution article despite different wording.
Pro Tips
Use GPT-4 for the response generation, GPT-3.5 for the retrieval — token-optimize
Add a "Was this helpful?" button to every response. Use the feedback to improve
Store the vector database in Supabase for zero maintenance
Discussion
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Common Mistakes to Avoid
Mistake: Not handling multilingual support from day one
Fix: Use DeepL for translation layer before the RAG pipeline. It costs $0.02/req and doubles your coverage.
Tools Used in This Playbook
Browse all toolsLangChain
Framework for building LLM-powered applications
ChatGPT
The most versatile AI assistant for daily tasks
Hugging Face
Open platform for AI models and datasets
Intercom AI (Fin)
AI customer support agent that resolves instantly