Setting up an AI chatbot for your e-commerce

In the modern e-commerce landscape, the inclusion of an AI chatbot can vastly enhance user experience, operational efficiency, and overall customer satisfaction. If you’re looking to embark on this transformative journey, here’s a step-by-step guide to help you get started.

1. Define Your Goals

Establish what you expect from your chatbot:

  • Providing 24/7 customer support?
  • Delivering personalized product recommendations?
  • Automating routine inquiries like order status checks?
  • Gathering insightful customer feedback?

Having distinct objectives will shape the entire integration process.

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Deep learning for quality controls of mechanical components

One of the many aspects where deep learning is revolutionizing the industry is that of quality control and fault detection.


This blog post describes the result of a collaboration between flair-tech and Qvision for the quality control of mechanical components. The problem is to check whether a spherical mechanical part, is correctly glued to a surface.


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Deep learning-powered medical image segmentation


Data and AI are reshaping the world. Deep learning is able to make sense of large amounts of unstructured data, such as visual contents. Automatically processing medical images is among the many applications of deep learning in healthcare.


This blog post describes the AI core of VERIMA, proudly engineered and trained by Flair-tech. VERIMA is a software solution that allows a surgical team to plan their surgery carefully. Its deep learning core is able to process raw CT scans and automatically identify bones, vessels and organs. Via a mixed reality visor, the resulting segmentation can be viewed and interacted with as a 3D hologram.

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Why anomaly detection is not binary classification


Anomaly detection refers to the problem of finding patterns in data that do not
conform to expected behavior.

—Chandola et al., Anomaly Detection: A Survey

Like a fish swimming upstream, the mouse befriending the cat and the rich giving to the poor.

But also like

  • the malicious user in your organization
  • the defecting engine you just manufactured
  • the fraudulent credit card transaction you just processed
  • the unreasonable network traffic on your servers

It seems that Anomaly Detection amounts to all but separate the good from the bad. And in fact, it is. So why not dig up the good old binary classification?

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