FAQs Using Deep Learning
Smarter Customer Support with AI-Powered Query Matching
Improving response time and customer satisfaction through semantic search and deep learning.
Project Overview
Managing a massive volume of FAQ data was overwhelming the client’s support system. Manual searching slowed down user experience and often left queries unanswered—impacting customer satisfaction and retention.
Language:
Python
Models:
Deep Learning, NLP
Cloud Platform:
Google Cloud
Address:
United States
The Challenge
With thousands of FAQs stored on the website, customers faced:
Delays in finding relevant answers
Decreased support efficiency
A poor overall experience due to generic responses
Our Solution
We developed an AI-powered FAQ matching system using NLP and deep learning. It identifies the most semantically relevant response to a user’s query from a large set of FAQs.
Key Highlights:
Semantic similarity using sentence embeddings
Cosine distance to measure query relevance
Real-time response powered by Google Cloud
How It Works
User submits a question
AI engine converts it into a sentence embedding
System compares it to stored FAQ embeddings
Closest match is returned instantly
Impact & Results
Relevant answers delivered in real-time
Significantly reduced search and response time
Enhanced customer experience
Client Outcome
Faster, intelligent FAQ handling
Reduced workload on support staff
Boost in customer satisfaction and retention
Project Snapshot
Language: Python
Models: Deep Learning, NLP
Cloud: Google Cloud
Location: United States
Industry: Customer Support / Web Services