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AI & ML — Case Study
Illustrative example — not a real client
AI Support Chatbot with RAG
A RAG-powered support chatbot trained on a company's own docs
40%
Tickets Deflected
<2s
Avg Response Time
5 Wks
Delivery Timeline
Industry
SaaS / Customer Support (Illustrative)
Services
AI Development & Integration • SaaS Product Development
Technologies
Python • OpenAI API • Pinecone • FastAPI • React
// The Challenge
A growing SaaS product's support team was answering the same category of documentation questions repeatedly, with response times slowing as ticket volume grew.
// Our Solution
Built a Python-based retrieval-augmented generation (RAG) chatbot that indexes the company's help docs in a vector database and uses GPT-4 to answer support questions with source citations, escalating to a human when confidence is low.
// Key Features
RAG-based Document Retrieval
GPT-4 Answer Generation
Source Citation in Responses
Low-confidence Human Escalation
// Results
Estimated 40% of incoming tickets deflected by self-serve chatbot answers
Sub-2-second average response time
Automatic escalation to human support when confidence is low
Project Duration
5 Weeks (Illustrative)
Engagement Model
Illustrative Example — Not a Verified Engagement
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