FLAIR aims to translate new research insights, results and technologies in the field of AI and Robotics, developed together with industry partners, into successful products and services.

The spin-off process is an important means of transferring and commercialising those technological innovations.

FLAIR is welcoming interested parties, i.e. investors and industrial partners to explore the creation of successful and sustainable companies, contributing to a flourishing innovation eco-system for the industry in GBA area. 

FLAIR projects selected by Hong Kong Science Park’s Ideation Programme 

To advance on the entrepreneurial journey, FLAIR has joined the Ideation Programme setup up by Hong Kong Science Park, a one-year startup support programme for tech-focused entrepreneurs: 

FLAIR Technologies


Potential Scenario

AI Platform - AI Vision and Data Management Platform

.A no-code AI vision algorithm creation training and management platform that supports AI vision accessibility with a low learning curve.

.Functions run on an easy-to-use UI platform which allows the user to use innovations such as intelligent labelling, online interference prediction, multiple options for platform deployment, flexible model selection, and smart tuning.

Spaces where there is no experience with AI vision and can greatly accelerate the implementation of an AI vision application

An AI Vision Inspection Platform for Model Training and Image Annotation

.A web-based machine learning platform that powers fast development of custom visual inspection models and automatic annotation of industrial images.

.Help manufacturers to automate their quality control process effectively and save labour cost from manual inspection and labelling of product images.

Manufacturing production line as the quality control unit or used by online vision system developers and data scientists

ARGUS - Agile Robotics Gridding Universal System

.A flexible quality assurance system that completes complex multi-faceted surface inspection and refining.

.Reduce lead time in vision setup by self-adapting to different object appearances with AI without labelling and using a small training data set.

High-mix low-volume manufacturing space

ILPEPS - Intelligent Logistic Process Evaluation and Predictive System

.Supports decision-making in process-based facilities by helping operators identify unsatisfying process performance, detect process bottlenecks, and recommend solutions for bottleneck root-causes by using digitalised and intelligent technologies.

.Functions are run on a visual interface allowing easy implementation in the workspace of veteran and new facility managers alike.

Can be adapted to manufacturing, commerce, and logistics companies to predict and prevent conveyer belt breakdowns