We analyse complex datasets, build forecasts and create tools that help teams make better decisions and run the business faster.
We work with companies in Belarus and remotely with project teams across Europe and the CIS.
What we do
We select technology for the business challenge and existing infrastructure — never the other way around.
We study sales, customers, equipment, inventory and other datasets to uncover patterns that are difficult to spot manually.
We design systems that process large volumes of information quickly and give teams a clear interface for action.
We start with the real state of your systems and propose a clear improvement path without unnecessary complexity or cost.
High-load systems
We design storage, processing, search, monitoring and security as one system. We scale where the challenge genuinely requires it.
Practice
Specific challenges and measurable outcomes delivered after implementation.
Analysed the history of rental prices.
Produced pricing recommendations for the next period and a repeatable recalculation workflow.
Automated background replacement across a large product image catalogue.
Built a reproducible batch-processing pipeline instead of editing each image manually.
Built a research prototype for recognising signals from sensor data.
Tested the technical hypothesis on a prepared dataset without making a clinical-use claim.
Optimised the database and accelerated chart loading.
Reduced response latency and removed noticeable pauses from the user workflow.
We built a research ML startup for analysing X-ray images and published the code and setup instructions so anyone can study the approach, run the project locally and test it.
View OsTrace on GitHubData questions
Sales, transactions, customer data, equipment metrics, inventory, logs, text, images and other structured or unstructured sources.
Not always. We first assess data quality and completeness, define a measurable hypothesis and determine whether the existing dataset is sufficient for a prototype or production model.
We agree on a business metric and technical indicators, compare the model with a baseline, test it on held-out data and monitor quality after launch.
We deploy the model as an API, service, Telegram bot, monitoring layer or module inside an internal system, then connect it to existing data sources and business processes.
Start with data
In the first meeting, we review data sources, constraints and the outcome that is worth measuring.
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