SupportBot AI
An AI-powered customer support chatbot handling common inquiries with OpenAI integration, knowledge base retrieval, and intelligent ticket escalation for 24/7 support.
Client
HelpDesk Pro
Role
AI Integration
Timeline
6 weeks
Team
2 developers
Overview
HelpDesk Pro's support team was overwhelmed with 300+ tickets daily, causing 24-hour response times. Many were repetitive questions about billing, refunds, and password resets. They needed an AI chatbot to handle routine inquiries and escalate complex issues to humans.
Process
Built context-aware chatbot using OpenAI's API with retrieval-augmented generation (RAG) from internal knowledge base. Implemented confidence scoring for escalation and integrated with existing ticketing system.
Key Features
Challenges & Solutions
Implemented retrieval-augmented generation (RAG) using vectorized knowledge base, added confidence scoring, and created guardrails to refuse questions outside scope. Accuracy improved to 97% with 0.2% hallucinations.
Implemented conversation memory using Redis, maintained context window, and structured prompts to reference previous messages. Context retention improved to 99.5%.
Improved prompt engineering, added follow-up question capabilities, expanded knowledge base, and adjusted confidence thresholds. Escalation rate reduced to 22%.
Optimized prompts, implemented request caching, used cheaper GPT-3.5 for simple queries with GPT-4 for complex ones, and added rate limiting. Cost per conversation reduced to $0.08.
Results
Support Tickets
human handled
Response Time
average
Response Accuracy
reliability
Team Capacity
without hiring
Satisfaction Score
out of 5
Labor Savings
first year
Goals
- •Reduce support ticket volume by 40%
- •Improve response times to minutes (not hours)
- •Maintain high accuracy in responses
- •Minimize hallucinations and false information
Tech Stack
- •Python
- •OpenAI API
- •Node.js
- •PostgreSQL
Target Users
- •Customer support teams
- •Help desk agents
- •Customers
Key Learnings
- •RAG (retrieval-augmented generation) is essential for accurate AI responses
- •Confidence scoring + escalation thresholds protect against bad outputs
- •Prompt engineering is an art—small changes dramatically improve performance
- •Cost optimization through model selection and caching is critical for profitability
Future Plans
- •Add voice support (phone/voice chat)
- •Implement sentiment analysis for emotional support
- •Build chatbot training dashboard for easy knowledge base updates
- •Add proactive support (reaching out to customers with issues)