We build human-machine workflows that utilise Machine Learning, Computer Vision and Natural Language Processing.
Improve decision making with AI document reader. Machine Learning based Natural Language Processing learns semantics behind events & key facts, and automatically extracts them for human experts to evaluate.
Detect anomalies and prevent financial losses by a Machine Learning model for cash flow patterns. Create a robust AI-driven fraud detection process for higher institutional security.
Detect problems before downtime and monitor current usage of the machinery with big data from IoT sensors. Machine Learning based systems prevent failures and help optimise usage by enabling more efficient maintenance.
Improve autonomous flaw detection with Machine Learning based computer vision that learns visual features of each passing flaw, and suggests low production cases to the quality control expert.
Leverage cutting edge technology and ready-to-deploy AI models for faster data processing, quick iterations, simple proof-of-concepts and easy deployment with a continuous learning, with a human in the loop.
One objective that central banks have is to identify bank distress and bank vulnerability. A Machine Learning model can analyse data from historical distress events, and take into account more than a thousand risk indicators and bank interconnectedness factors. These types of solutions can be used at central banks to help financial analysts and regulators. A model we created for the European Central Bank demonstrated as much accuracy in its predictions as 7000 human analysts, outperforming all existing systems.
Consumers are expecting increasingly more personalised shopping experience, especially when it comes to online shopping. If they don’t see something relevant for them immediately, chances are they’re going to leave. Machine Learning based recommendations systems can be used to tailor and personalise the shopping experience for each individual based on the consumers’ past interests and actions. This enables intelligent targeting not only within web but also through different marketing campaigns.
When making investment decisions, financial experts need to look for risks and opportunities by reading hundred-page documents, a time-consuming process prone to human error. An Natural Language Processing based AI model can learn the semantics behind key events and facts, and automatically extract the key risk-indicators for financial experts to evaluate. The AI generates a summarisation of its findings to be validated, improving process throughput time and gathering the entire department’s input into a single learning system.
Impact investing – funding projects that generate investment returns, alongside socially and environmentally beneficial outcomes – holds many opportunities for AI. For example, Natural Language Processing and other AI techniques can be used to analyse a large variety of data sources for pre-investment vetting. Investing becomes more data-driven when scraping, aggregating and analysing the data regarding potential target investments is systematic. Also, a major manual bottleneck in the pre-investment vetting process is removed.
In large-scale recruiting AI can be useful both to scout out new potential employees and to help retain the good hires. Large-scale recruiting generally follows a structured, multi-staged process, which can be improved in many ways. AI can predict employee success based on historical data from the previously hired employees, increasing the efficiency of discovering and selecting high-quality applicants. AI recruiter adds an additional level of getting to know the candidates, leading to less time spent in the interviews. In addition, AI recruiters can bring out biases in the human recruiters, that would otherwise go unnoticed.
The key to profitability within ad tech is the ability to make precise and fast predictions on whether to buy/sell particular impressions. Even though there are plenty of data available, there are several challenges in being able to do this in practice. These predictions need to happen within milliseconds and as the complexity of models increase, they get also computationally more expensive to use. With the right kind of expertise on Machine Learning infrastructure and modeling, there are profits to be made especially by deploying techniques that aren’t yet widely used, e.g. deep learning and reinforcement learning. These techniques can be applied to constantly search for the most optimal decisions in the dynamically changing marketplace.
In an environment where constant monitoring of a vital system is required, a AI can help spot possible problems. For example, sewage pipes can be monitored by video cameras, and this video data can then be processed through a Computer Vision model. The AI model recognises probable pipe defects, anomalous objects and blockages in the video and flags them. Human experts give the model feedback by either confirming or rejecting the potential problems, thus further improving the accuracy of the model and data quality for future use. The defect alerts can be then used to guide human activities such as maintenance visits.
In the legal industry the biggest asset of any company is the expertise of its employees. This expertise is captured in countless number of documents and metadata related to them. Capturing the collective experience of what has been developed by the company in the past for various cases forms the core of AI-assisted legal work. Law firms can significantly cut down the time used for getting started with a document by making better use of what’s already known to be a good approach and even clause level formulation for a specific situation. This speeds up new lawyer on-boarding and allows tapping into the knowledge often available only through discussion with senior lawyers.
Industrial production is a prime target for AI from a variety of perspectives. A production line AI brings value to the core production processes by intelligently combining data from different sources, optimising the production capacity and reducing the number of defects. With production line AI, you can identify common error patterns and scenarios in order to decrease line downtime, material losses and quality variation, but also to analyse idle time and optimise usage of production line machines with predictivity. The AI system continuously improves its level of automation and accuracy as it learns from the human’s feedback on its output.
In airlines and maritime industries AI can be used to support operations, predicting problems and enabling a holistic view of current conditions. For example, an AI augmented control center can help in spotting issues before they become a problem. A typical problem that can be predicted with AI includes operational delays, that can be due to a magnitude of different reasons.
Shipbuilding facilities can benefit from AI systems in many ways ranging from energy efficiency management, predictive maintenance to system fault to error identification. Also, autonomous vessels are considered to be future of maritime industry. For this vision to materialise, intelligent systems are required to monitor both the vessel’s own operations as well as its environment. It’s vital that ships are able to understand and communicate what is around them and make decisions based on that information, aided by humans. Computer vision models are used for example for object detection and classification, in order to make sense of the ship’s surroundings.