AI-Driven Customer Service Bot For Retailers

PROBLEM
Current retail searches use filters, which can be unintuitive. Introducing a chatbox can simplify searches, but the key challenge is to ensure precise customer-product matching in this new format.
PROJECT GOALS
Introduce an AI chatbot in retail for quick item searches, aiming for 80% accuracy in customer-product matching to decrease search time and enhance user satisfaction.

SOLUTIONS

NLP with OpenAI API/GPT4

We used NLP models for intent recognition and named entity recognition to understand user queries and extract specific details, improving search accuracy.

Machine Learning with TensorFlow

We utilized machine learning for recommendation systems and query expansion. This analyzed user behavior and expanded search scope based on user inputs.

Computer Vision with YOLO

Implemented visual search allowing users to upload images or describe products visually, enhancing search capabilities beyond text.

Integrations

Developed a chatbot using OpenAI APIs for natural conversations and integrated it with retailer systems through APIs for real-time access to product information.

BENEFITS

Enhanced User Experience & Engagement

The chatbot offers a user-friendly search interface and fosters engagement by providing an interactive shopping experience. It also personalizes recommendations based on individual user preferences.

Efficient Search & Accurate Matching

AI technologies like NLP, machine learning, and computer vision expedite the search process and accurately match customer queries with relevant products, hitting an 80% accuracy rate.

Conversion & Discover

The system converts searches into purchases through tailored suggestions, facilitating effortless product discovery and contributing to increased conversion rates.

Adaptive Learning

The chatbot employs machine learning models to continuously learn and adapt to evolving user preferences and a growing product catalog, ensuring the system remains relevant and effective over time.
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