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
- Industry
- Customer Support
- Type
- AI Chatbot
- Year
- 2025
- Our 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.
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.
What we built.
- featAI-powered natural language responses using GPT-4
- featKnowledge base integration with semantic search
- featConfidence scoring for query understanding
- featIntelligent ticket escalation to human agents
- featMulti-language support (English, Spanish, French)
- featConversation history and context retention
Hard parts.
fixInaccurate and hallucinated responses
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.
fixContext loss in multi-turn conversations
Implemented conversation memory using Redis, maintained context window, and structured prompts to reference previous messages. Context retention improved to 99.5%.
fixHigh escalation rate (70% of conversations)
Improved prompt engineering, added follow-up question capabilities, expanded knowledge base, and adjusted confidence thresholds. Escalation rate reduced to 22%.
fixCost of API calls spiraling
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.
Built with
Python OpenAI API Node.js PostgreSQL
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