01
Quality and object inspection
Defect discovery, completeness checks, object detection and counting in images or video streams.
- Detection and segmentation
- Process inspection
- Signals for an operator
Computer Vision
Computer Vision for business and applied systems
We build solutions for object recognition, quality inspection, image data extraction and video analysis — from hypothesis testing to integration.
We work with companies in Belarus and remotely with project teams across Europe and the CIS.
- 01Images and video
- 02Classification and detection
- 03API · edge · private cloud
Use cases
Computer Vision where manual review does not scale
Model choice is only part of the solution. Capture conditions, data, labels, acceptable errors and the operator's role are equally important.
02
Documents and visual data
Field extraction, image classification, catalogue preparation and automation of repetitive visual processing.
- Classification
- Data extraction
- Batch processing
03
Applied and research models
Prototypes for complex imagery with explicit limits, data-quality requirements and a clear permitted use.
- Hypothesis testing
- Separate validation set
- Clear prototype-to-product boundary
Path to launch
From data quality to a model that works in the real environment
We first establish whether the target signal can be observed reliably in the available data, then select the model and infrastructure.
- 01
Scenario and errors
Define the useful outcome and the cost of false decisions.
- 02
Data and labels
Review the sample, capture conditions, classes and label quality.
- 03
Baseline model
Compare approaches on a separate validation set.
- 04
Integration and monitoring
Integrate an API or edge module and track quality after launch.
Deliverables
A testable solution, not a single accuracy number
We show which data was used for validation, where the model fails and what production operation requires.
- 01
Data, class and limitation description
- 02
Model and reproducible validation workflow
- 03
API, service or edge module for the use case
- 04
Quality monitoring and team documentation
Open development
OsTrace — a research prototype for analysing X-ray images
The code and setup instructions are public on GitHub. OsTrace is not medical software, has not undergone clinical validation and must not be used for diagnosis.
Computer Vision questions
What to validate before model development
Which problems can Computer Vision solve?
Object detection and classification, quality inspection, defect discovery, data extraction from images, video analysis and decision support inside applied systems.
How many images do we need to start?
There is no universal number. It depends on object diversity, capture conditions and the required quality. We first assess the dataset, labels and whether a suitable pretrained model can provide a baseline.
Can images and video be processed locally?
Yes. Depending on latency, privacy and hardware constraints, a model can run on a server, in a private environment, in the cloud or on an edge device close to the data source.
How do you evaluate model quality?
We define the business scenario and appropriate metrics, keep a separate validation set, examine different error types and test the solution under conditions close to real operation.
Can we begin with a research prototype?
Yes. A prototype tests the data, constraints and value of the scenario before production integration. Its status and limitations are kept clearly separate from a production-ready system.
First step
Show us the data and workflow — we will assess whether the scenario is realistic
In the first meeting, we review the image or video source, capture conditions, acceptable errors and a practical way to validate value.
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