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We develop fully-automated and scalable visual technology by combining our R&D in deep learning and computer vision with a performing infrastructure architecture and a customer-driven approach.

We were a pioneer in image search and image recognition, having started development during our times in NExT, prior to our spin­off in 2012.

Nowadays, we are the first to develop a combined in­-video search and recognition that is fully automated.

Moving forward, we’re keen to use our visual capabilities and artificial intelligence to develop analytics that are able to identify macro and micro trends and segmented behavior patterns and make intelligent recommendations and predictions.

Our visual tech solutions

Visual search

We show visually similar items without using any keywords or metadata.

Image Search

We perform reverse image search, allowing users to utilize uploaded images or their links as search queries instead of keywords. Then we return visually similar images (in terms of color, pattern and style) by analyzing the pixels inside the image instead of the metadata associated with it.

For even better results, we add automatic object recognition of objects within an image query, background noise removal, as well as human body detection and skin removal for the fashion industry.

In­-video search

When users watch a video, we can surface visually similar products to those found in the video.

Alternatively, we enable advertisers to place relevant contextual ads on videos. We enable that by matching the visual content found in the ad with the visual content found in the video.

Visual recognition

We do object recognition, product tagging and logo recognition in both photos and videos.

Image recognition

By feeding adequate training data, our machines are able to identify, tag and describe the objects found in images, no matter how large the database is.

In­-video recognition

We are able to identify objects and their attributes in video content, provided we have the video links or files and a clear categorization purpose and training data.

Our customers can rely on us for a high-standard visual technology, defined by a simultaneous delivery on:

Scalability

Our infrastructure architecture enables us to automatically scale up to support the indexing and processing of billions of images, without sacrificing on other performance parameters.

Latency

Our results are generated in 100 to 200 milliseconds for search based on an existing database item, and up to 1 second for search based on a newly uploaded image.

Accuracy

Our ability to search and find matching and similar results was evaluated at above 90% satisfaction rate by QA analysts from our customers side.

Reliability

Our service delivery architecture is designed by putting redundancy and failover in place to achieve high availability and ensure good service levels.

Key elements of our state-of-the-art technology

Deep learning algorithms for computer vision applications

As pioneers, we implemented deep learning for visual search in the retail sector since 2012, and expanded its use to new applications over time.

Convolutional neural networks

We use the latest type of artificial neural network, inspired by biological processes.

Supervised learning

Our machines are self-taught on how to recognize objects and attributes based on examples provided by our customers.

Training data

Our algorithms are constantly being improved while performing tasks on large databases.

Cloud-based infrastructure architecture

Our R&D scientists and infrastructure architects work together to achieve the best performance for our B2B customers.

Parallel processing

We use multiple queues and parallel processing to ensure the same speed for all concurrent users.

Cloud-based servers

We auto-scale whenever required by using a distributed architecture and cloud-based servers.

Deep learning GPUs

We use specialized NVIDIA GPUs for our deep learning algorithms and high performance power.

Our R&D and Engineering team background

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Spin-off from NExT

NExT is the leading research center in the area of multimedia analysis and search established by National University of Singapore, ranked 22nd in the world, and Tsinghua University, ranked 47th in the world.

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10 PhD scientists

Our R&D, Infrastructure Architecture and Dashboard teams count 20 talented computer engineers, out of which 10 are PhD scientists.

Guangda Li, our CTO, has been selected by MIT Technology Review - EmTech as finalist in Top Innovators Under 35.

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Industry publications

Our scientists have published their research papers in deep learning and computer vision journals.

Talk to us about the present and the future of our artificial intelligence and how it can help your business.

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