Dynamic Quick Filters with AI

Machine learning service that personalizes and ranks filter options for users in manufacturing

#Industry & Energy #AI #Python #NumPy #PyTorch #MLModel

Project definition

Location

North America

Client

Industrial company

Project type

ML model

Industry

Industry & Energy

Service list

ML Engineering / Computer Vision

Team size

S-Size (1–3 engineers)

Budget

$50,000 – $250,000

Task

Users experienced overload due to the full list of filters: finding the desired option took up to 60 seconds, negatively impacting engagement and conversion. The goal is to simplify the interface through personalized filtering

Solution

FTECH-IT developed a ranking service that generates a top-10 list of the most relevant filters for each user based on the analysis of their behavior

Impact

The ranking service reduced filter search time by 40%, boosting user engagement and increasing conversion rates. Personalized filtering led to a more intuitive interface, directly improving customer satisfaction and business performance

Python 3.x

pandas

NumPy

Scikit-learn

Redis

Docker

AWS EC2

AWS S3

Optimizely

Datadog

FastAPI

Flask

💡 This is an AI-CORE project

Ftech-it, as an AI-powered company, knows better than anyone how to design and implement deep AI architectures that deliver measurable business impact and uncompromising reliability

💡 This is an AI-powered project

Ftech-it, as an AI-powered company, understands better than anyone how to integrate intelligent components into real products, ensuring speed, accuracy, and seamless production-grade performance

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Project development highlights

Search Time Reduction

The ranking service cut filter selection time from up to 60 seconds to a few seconds per user

Engagement Boost

Personalized recommendations improved user engagement with the platform

Conversion Rate Uplift

Simplified filtering contributed to measurable increases in conversion rates

Efficient ML Deployment

Rapid ML model inference delivers real-time filter ranking without user delays

Development Process

We developed a ranking service powered by Python, PyTorch, and scikit-learn that analyzes user behavior and generates a personalized top-10 list of relevant filters in real time. The backend was implemented with Flask/FastAPI and supported by Redis for caching. Deployed on AWS EC2 and S3, the system ensures both speed and scalability, while Datadog and Optimizely provide monitoring and experimentation frameworks. This AI-powered approach delivered a tailored experience that simplified complex workflows and improved overall platform performance