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A complex solution automates cholesterol plaque detection in artery scan videos, helps calculate the intima-media thickness, and makes analysts work faster - all using the computer vision. The artery wall thickness is calculated with 95% accuracy, potential plaque detection is 80% correct. As a result of the project, the solution price became more competitive due to savings on expensive human resources.
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Our customer's conversion rate had become stagnant for a long time, despite their travel experts’ efforts, premium quality, competitive prices, and customer care. In order to increase the conversion rate, we developed an ML-based module to detect leads with the highest marginality.
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The IoT and ML-based solution enables farmers to effectively control the conditions of animal housing and raising, and to competently manage resources. It brings insightful data together, harmonizes and delivers it to farmers in an easy-to-observe and interactive manner.
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The module detects fraudulent activities (e.g. password guessing) and protects a high-loaded telecom web-service. The solution is designed to handle a huge amount of input data, so load and performance tests were a major part of the project, as well as functional and manual testing.
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Ranking and distributing client requestsEvaluation of client requests on ecommerce platforms, and their distribution by marginality and probability of transaction. Quality requests (“hot leads”) are sent to the sales department to be processed first. According to our data, in some retail projects conversion rates double if a prospect is followed up within 15 minutes of the request.
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Prediction of expensive incidentsA data-based graph of anticipated expensive incidents is created (for example, a client defaulting on a loan, the occurrence of an insured event, or other events that affect your business). Managers get alerted about the increased risk of an expensive incident in advance, allowing them to make data-driven decisions, while vulnerability to undesirable events decreases.
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Anomaly detection and predictionAnomalies can be detected in any data. For example, integration with a medical video scanner makes it possible to rapidly find several informative frames and reduce the probability of medical error, as well as speeding up the work. In logistics and shipping, anomaly prediction prevents collisions and flooding of ships, and increases the safety of transportation.
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Recommendation systemsUsers get recommendations for purchases based on decisions made by them and by similar groups of customers. Everyone gets access to the most suitable products, regardless of whether a visitor is known or anonymous, and the store provides highly personalised recommendations. The average amount spent increases, and the number of repeat purchases grows.
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Implementation of a custom information system based on machine learning.
Addition of a machine learning module to an existing information system.
Machine learning algorithm development as part of a globally distributed team.
If you would like to assess the possibility
of adding machine learning to your system,
please contact us at
Skype calls and virtual screen sharing: we talk with our customers every day. Each week, project teams meet up for a weekly demo, sprint planning, and Q&A sessions.
80% of our customers are return buyers. Most of our clients recommend our services to their partners and colleagues.
A personal manager with fluent English will facilitate the communication between you and the development team. Teams use use customers' release management tools (Jira etc.)
Why? Good salaries, employee benefits, and a healthy working environment! We often send our staff for training and take pleasure in their progress.
You get transparent project management, along with a detailed risk map. You also get a list explaining project expenditure.
How we process your personal data
