A powerful process automation platform provider was trying to enlarge their development team. Peoly provided them with a managed nearshore development team with experience of integration, RESTful API and JavaScript as well as knowledge of front-end development. After an evaluation period, we were able to qualify as an exclusive partner for the project. The assignment was to integrate 3rd party APIs into the customer platform. Within a couple of months we had expanded a dedicated team of 5 to 15 people with a specific developer profile tailored to the customer needs. Currently, the customer makes use of a wide scope of our services and enjoys access to a team extended on on-demand basis.

A customer with a long term experience in AI/ML (bylo ML/AI) was looking for highly qualified Machine Learning developers experienced in real-time object detection and tracking, as well as behavioral analysis of objects in video feeds from a retail environment. We created an expert team comprising an ML specialist with experience of real-time object detection pipeline (detection model, transfer learning, tracking, behavioral analysis), two senior developers with experience of real-time object detection pipeline and Nvidia DeepStream SDK principles, and one annotator with experience of preparing ground truth data for ML models. Our team has substantially helped improve current customer solution in terms of performance, reliability and speed of processing.

A furniture aggregator platform was looking to extend their search services with visual search, which allows customers to find similar products just by uploading a photo they have taken or downloaded. We prepared the whole service as a separate microservice for the customer’s Kubernetes infrastructure. The team consisted of two ML experts and two Senior Developers, who completely designed and developed the whole service including the communication model with other already existing services. We worked with the latest architecture designs such as detector, feature vector extractor, approximate nearest neighbor and other services.

A client specialising in Facial Recognition approached us with a project on optimization of Deep Neural Network (DNN) models for TensorRT to enable high performance inference on NVIDIA GPUs. One of our tasks was finding a balance between computation load and precision for DNN quantization processes. We prepared AI video analytics pipelines with features such as object detection, classification and recognition. These pipelines can be extended by additional DNN approaches (e.g., tracking) and are based on DeepStream SDK with custom GStreamer plugins built by our company.

Our in-house Natural Language Processing (NLP) solution can analyse thousands of documents a day. It determines the type of content in the documents (while respecting their privacy) and can store them in a database according to predefined content types. ​For example, if you had 10,000 unlabelled documents in one folder, you could use the PDF classifier to sort them into four different folders automatically, without having to open a single PDF.

We created an app which listens to unusual sounds coming from cars and provides a diagnosis of the underlying problém. It is a unique technology which provides a 98% accuracy after training on extremely small data sets of only a few sound samples.