Access to large amounts of data, faster computers, and advanced machine learning techniques already made it possible to successfully apply AI technologies throughout the economy: threat intelligence and prevention, medical diagnosis, automatic translation and chat bots, shopping and on-site personalization, facial recognition and smart speakers, AI-powered recommender algorithms, data-driven predictions and insights, the list can go on.
One of the factors often credited for this latest boom in AI investment, research, and related cognitive technologies, is the emergence of deep learning neural networks as an evolution of machine algorithms, combined with computing power and accumulated knowledge that makes deep learning a practical reality.
It won’t be long before the use of AI is assumed for every application in every industry. Join the AI revolution!
Artificial intelligence has come into the mainstream
AI is accessible to small and mid-size businesses
84% of enterprises believe AI will lead to greater competitive advantages
Demand for AI talent has doubled in the last two years
By 2021, 80% of emerging technologies will have AI foundations
Here's how we do it
One or several workshops are conducted with the purpose of identifying business areas where AI implementation would have the largest impact.
We investigate the suggested tasks to determine whether the latest AI solutions would be able to deliver expected results within given timeframe and budget.
Many data science and AI approaches can be used to solve business problems. At this stage we experiment and select AI model(s) and tune them to address your specific needs.
Data collection for training could be the most expensive part of the project. Our aim is to obtain datasets at minimal cost, we synthesize such training datasets where possible.
We develop a solution prototype and verify that this concept works in the environment that is simlar to target environment.
We put our software development expertise into bringing all bits together: AI models and custom software fully connected to your business systems.
Hardware and software combined is deployed to production environment. To reduce operational costs your application may use cloud services.
We organise knowledge transfer and provide ongoing support and maintenance of your custom solution.
The purpose of image classification is to assign images one class from each of predefined groups.
Many businesses possess huge databases with visuals which are difficult to manage and make use of. That's where image classification steps in, by automating the sorting and tagging process, be it for fashion apparel, medical imaging or landscapes.
Given an image an object detection model can identify which of a known set of objects might be present and provide information about their positions within the image.
The first thought that comes to mind while talking about object detection is self-driving cars and for good reasons. But the possibilities are endless when it comes to future use cases for object detection.
Image Segmentation is a process of dividing an image into sub partitions to make it easier for machines to understand, process, and analyse.
Object Extraction is a closely related issue with the segmentation process. The main goal of object extraction is to change the representation of an image into something more meaningful.
Object tracking systems analyse consecutive movements of an object in video frames, for example tracking a person or tracking a ball during a football match.
Object tracking has a variety of uses, some of which are surveillance and security, traffic monitoring, or detecting customer choices in a store.
Human pose analysis predicts the positions of a person's joints in an image or video.
The ability to do bio-mechanical analysis in real time opens the road to many implementations, think advanced driver assistance systems or sport analysis. Another example is assisted living, sudden fall can be detected and addressed immediately saving people lives.
Segmentation, classification, data extraction of text in a document.
Document type recognition is one of the examples of usage: detect and classify a document (credit card, drivers license, etc.) and extract the document fields, including images, for further processing or storage.
See visualisation videos for some of our AI R&D projects:
The purpose of this project was to develop an AI model that is capable of recognising, identifying and counting products on a shelf.See Video
The video demonstrates how AI model responds to product movement and product placement area resizing events in real time.See Video
The purpose of this R&D project is implementation of an AI-based system that is capable of monitoring operational compliance to safety standards in real time.See Video
"AI is going to change the world more than anything in the history of mankind. More than electricity."AI oracle and venture capitalist Dr. Kai-Fu Lee