Custom AI Software Development Services

The task of our team was to develop a Speech-to-Text model for an application with speech recognition functionality for teaching children to read. It was also required to make sure that the app would be able to control children's pronunciation. Our AI development company collected and augmented intensive data sources, designed custom architectures, successfully deployed the solution and tested it. We integrated several sound transfer techniques for speech pattern reconstruction and built clusterization algorithms for data segmentation and further analysis.

Our team was responsible for building an NLP-based solution aimed at streamlining the process of editing and proofreading text data. We defined text types to customize our suggestions by means of different classification approaches. We used an algorithm to notify users when a part is unexpected for the defined text domain and offered corrections based on our knowledge base. Also, our custom AI development company worked on a system for extracting key concepts from texts with the help of TF-IDF, LSI, SOTA.

The Cogniteq team was hired to build a solution that would help a fintech company improve the assessment of non-return loan risks. The built solution accumulates data about the client’s recent loans and overall credit score. After that, the collected historical data is processed by ML algorithms to provide forecasts about the particular client’s capabilities of covering the requested loan conditions. The data is gathered from various sources including public records and our client’s history of interactions with the particular customer.

Our developers had to create an AI-based operating system that could unify and process business data. The operating system unifies all the data coming from different sources, sorts it into multiple categories, and analyzes it with the help of ML algorithms. It enhances various businesses with more efficient data-based decision-making tools, from market analysis to threat intelligence. Our AI software development company was responsible for the project architecture and database design, as well as the development of microservices and system asynchronous parts.

We needed to develop an AI engine that would be able to provide customers with relevant ads by identifying the group they belong to. The delivered solution analyzes and groups users using semantic embeddings from the BERT family of models and various clustering methods. After identifying which group a user belongs to, the solution selects the most relevant ads based on similarity scores from the transformational models. New ads are generated with the help of the latest transformational autoregressive models to fill gaps in existing ads.

Our task was to design an algorithm for the personalized selection of profiles which was expected to change the way the existing dating website worked. Now, the website offers users a detailed relationship-oriented questionnaire and the ML algorithm seeks matches based on numerous parameters. The Machine Learning system explores user behaviors to find out people’s preferences. Image analysis of the face in user photos helps to adjust the algorithm so that users are matched with profiles they are likely to find appealing.