Machine Learning
How we work
We join in at any stage.
At any stage of product development, we perform tasks related to prototyping, developing, and integrating apps using machine learning approaches, including strategy development, formalization of requirements, data collection, preparation and labeling, design of neural network models architecture, training models with in-house equipment and integrating ML models into the project. To build neural network models we use such frameworks as Tensorflow, Keras, PyTorch, scikit-learn, etc.
We focus on the client’s business goals.
Our client’s KPIs are our main priority. We develop and implement solutions for analyzing and optimization of marketplaces, e-commerce, and broker platforms. We work with structured and unstructured, statistical and flow data. We perform analytics and prepare documents, getting the infrastructure ready.
We practice agile development.
We work in short sprints (one or two weeks), and at the end of each sprint, we provide definite measurable results. Every sprint ends with a demonstration of the achieved results. Any corrections, if required, are quickly implemented into the strategy of working with the machine learning methods.
What you get
- labeled data set to train and validate neural network models;
- neural network prototype to solve your task with further improvement and optimization of the quality of model functioning;
- numerous model training iterations training using our equipment (or client’s equipment);
- plan for ML models improvement to upgrade the quality of their functioning;
- numerous experiments carried out with various neural network architectures;
- data-based justification of selecting one or another approach in optimal neural network architecture;
- integration of the trained machine learning models into IT products.
Why choose us
The Leading Global Service Provider
According to Clutch
IT Team of the Year
Stevie IBA 2020
Winning Company
in Technology Category
Skills and experience
Production and construction
– objects recognition in the drawings;
– energy consumption forecasting;
– workers’ action recognition;
– detection and prevention of emergency situations;
– automation of defect detection for production conveyor;
– forecasting necessary and sufficient production output.
Marketing and sales
– estimation of purchase probability;
– sales forecasting;
– speech recognition and transcription for call centers;
– seasonal demand forecasting;
– stock balance forecasting;
– an estimate of the probability of success in a tender;
– automated customer feedback collection from different channels;
– smart recommendations based on the customer cart content.
Agriculture
– detection and classification of plant diseases;
– automated cattle counting in the video stream in real-time;
– soil type detection.
Financial markets
– forecasting stock prices, exchange rates, and raw material prices;
– financial instrument trend prediction;
– financial instrument pattern prediction.
Logistics
– creating the best route;
– best weight distribution;
– scheduling;
– estimating wear and tear on a vehicle;
– driver state monitoring.
Banks
– classification of contracts;
– automated extraction of structured data from document images;
– credit scoring;
– insurance scoring;
– detection of fraudulent transactions.
Image processinf
– improving quality;
– removing background from photos;
– detecting anomalies in photo objects;
– detecting objects in the video stream;
– image segmentation;
– black and white photos colorization.
Generative networks
– generating watch model;
– generating images based on sketches;
– generating content for the site;
– generating music trends based on user preferences;
– generating design and home decoration;
– generating website design;
– generating logos.
