Software development solutions

Software development solutions
Software development solutions

These are the three main areas in which we operate, although a challenging project may be anything our client finds innovative and difficult. If this is your case, send us a message.


Machine Learning

We build robust and scalable Machine Learning solutions. We automate data processing and develop models and algorithms that become our clients' core IP. Knowing that the ML project can be complex and unpredictable, we lead you through it with a fully-managed process.

01. Defining the technical problem for a specific business need

We work with a client to achieve a high-level overview of the ML application and classify possible technical areas considering approaches to building the infrastructure and developing the ML model.

02. Conducting in-depth research/prior art analysis

We don't reinvent the wheel: we identify state-of-the-art research papers for related problems and analyze the strengths and limitations of the described approaches.

03. Selecting the solution technology

We identify the most efficient approach based on the problem, the research, and constraints (such as a limited data set). We don't strive for the fanciest solution: sometimes, a heuristic may be a better choice than sophisticated deep learning.

04. Developing and managing the data set

The importance of a good data set is quite often underestimated. It is often expensive and time-consuming to build. It is also difficult to manage it and ensure the data is consistent. We bring creativity in designing data augmentations and experience in data governance and management.

05. Designing, building, and training the model

It's common to validate dozens of model variations and run hundreds of training experiments. We combine ML skills with engineering excellence and passion for automation to keep this dynamic and highly experimental work under control.

06. Deploying and integrating the model

ML research results are only useful if they deliver business value to users. We deploy the models and expose APIs that other engineers can use without any knowledge of ML.

Machine learning projects are all about experimentation, rapidly testing various ideas, some of which are successful and others leading nowhere. We run hundreds of experiments with different hyperparameters, model variants, and training sets. After several months we need to identify which experiment performed best and which model variant was used there.

Usually, things get even more complex, as systems often use not one but a chain of linked models. We can control it only when following a well-defined, strict process supported by proper tooling - our ML Ops framework.

Data Engineering

Data Engineering

All the data is divided into datasets and stored in a central repository. Data management and maintenance are straightforward, and different ML models can be trained and validated on identical data samples for easier comparison.

ML Model Engineering

ML Model Engineering

We put ML models under version control. All the pipelines are automated, and test environments are customized and isolated. It makes experiment results reproducible, so we can focus on running more experiments and developing the most promising approaches.

Comparing Experiments Results

Comparing Experiments Results

Performance metrics defining the ML model's business usability are automatically collected and uploaded to the visual analytic dashboard. All the stakeholders have access to it, which speeds up choosing the approach we want to pursue.

ML Operations

ML Operations

Model deployment is automated and supports various integration and testing techniques.

Architecture & Backend

We can build your system from scratch, elevate and optimize your existing algorithms to match the business need, or deliver the technological upgrade of your early-stage product.

We have a history of designing and developing complex backend systems with numerous datapoints, processing high-frequency, time-series, or IoT data. With this experience, we pointed out the most important qualities of the systems we build:


We believe systems should be designed to adapt to increasing traffic. Our telematics solutions have successfully gathered and processed IoT data streams from hundreds of thousands of users.


Data leaks or unauthorized access may have a catastrophic impact on business. Our systems have passed external security audits to mitigate the risk of such black swans.


It may not seem that significant initially, but it's crucial for systems' long-term development and maintenance. Code quality is one of our biggest concerns, so we define strict technical standards and follow internal procedures that ensure their proper implementation.


Building systems is more than just design and implementation. It is also creating environments to seamlessly and predictably roll them out to production. We rely on DevOps practices heavily, from introducing CI/CD pipelines to infrastructure provisioning in early project phases.


The vendor’s job isn't just to deliver the solution. Production support is equally important. We define business and technical metrics to configure alerts that allow us to detect any unwanted events early.

Tech stack

Each problem is unique and should have an original technological solution. That's why we constantly learn, grow, participate in conferences, and certify. We work with modern technologies such as Kotlin, Go, Python, and Java and leverage cloud solutions.

R&D Prototypes

Building prototypes differ from developing production-ready systems. The goal is to verify hypotheses and design key algorithms based on research and experiment results. One possible outcome is creating a breakthrough patented solution, the other is proving the problem unsolvable.

Pairing our experience in research-based projects we gathered in scientific institutions with two decades of business product development, we're confident to carry out inventive business ideas.

Prior-art analysis

There might be a reference to similar cases in scientific literature, even for emerging problems. We dig deep into it to significantly shorten the solution design time.

Algorithm design

The essence of building a prototype is to propose innovative solutions and make a company's intellectual property. No matter how unique the case, the R&D team should always possess similar qualities - creativity, scientific background, and problem-solving skills.

Research project management

Prototype development is ambiguous, and managing such a project differs from typical software development. It's our area of experience, as we run numerous R&D projects financed by private funds and national research grants.

Defining the KPIs

While creating new algorithms, it's important to determine the proper metrics to monitor the progress. That's why we always define milestones at the beginning and set up indicators that guide us to the definition of done.

Solid engineering

Each project's purpose is to bring business value to our clients. We always follow good software engineering practices so that the prototype can become a part of a practical solution, not just a demonstration of an idea.