MachineLearning

Machine Learning (ML) is a technology that enables computers to learn from data and independently improve their algorithms without direct programming. For businesses, this means the ability to identify hidden patterns, make predictions, and make more accurate data-driven decisions. Thanks to ML, companies can increase conversion rates and customer retention, reduce costs, and optimize internal processes. The SKALAR team has world-class expertise in ML and knows how to turn advanced algorithms into practical solutions that bring real benefits to your business. In other words, we don’t just implement a model—we build an intelligent system tailored to your needs. Your project is in reliable hands with our expertise!

"Without machine learning and AI, companies lose touch with their customers, market and future." - Satya Nadella, CEO of Microsoft

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Аналітична експертиза SKALAR – фундамент успіху ML-проєкту

Successful implementation of machine learning projects requires thorough analytical preparation and experience working with data. According to experts, up to 80% of an ML initiative’s success is determined during the planning, data analysis, and requirement preparation stages. That’s why, at SKALAR, analysts with many years of experience are involved from the start: they meticulously study your business, data, and processes to form a clear vision of the future solution. Our specialists have been creating custom IT solutions and automation systems for over 11 years, and this accumulated experience allows us to immediately see the best ways to apply AI to your goals.

SKALAR analysts thoroughly assess your business processes and existing data, identifying “bottlenecks” and growth areas where ML can provide maximum impact. The result of this analysis is a formal set of requirements for the solution. We prepare key project documentation that sets a clear direction for the ML project:

BRD (Business Requirements Document) – Business requirements. Describes the goals and objectives of the project from a business perspective: which problem the ML solution should solve, what KPIs are planned to be achieved, and what constraints and risks exist.

FRD (Functional Requirements Document) – Functional requirements. Specifies the system’s features and algorithmic elements needed to meet the business requirements. It explains exactly what the ML system should do: which data to use, what models and quality metrics are required, and how it will integrate into processes.

PRD (Product Requirements Document) – A consolidated set of requirements. It brings together business and functional requirements into a single specification for the ML product. The PRD details all aspects of the future system: its functions and modules, data and model requirements, UX/UI (if the solution has a user interface), performance, security, integrations, and other important details for development and implementation.

With carefully prepared documentation at the start, all project participants—client, analysts, data engineers, developers, and ML specialists—are on the same page. This reduces the likelihood of errors during development and saves time and budget.

Key Stages of an ML Project Implementation

Identifying and Prioritizing Business Goals

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We start by immersing ourselves in your business: conducting interviews with stakeholders, studying the company’s strategic goals, and identifying the problems to be solved with AI. It is crucial to define priority tasks where machine learning will bring the greatest value, ensuring the project is focused in the right direction.

Process and Data Analysis

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Our analysts examine existing business processes and assess the available data. We determine which processes can be optimized with an ML solution and audit the data: whether there is enough historical data, the quality of the data, and whether additional sources are needed. The result of this stage is a clear understanding of which ML models are appropriate and what data needs to be collected or prepared.

Requirements Formation and Data Preparation

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Based on the analysis, we compile a list of requirements for the future system: its functionality, key efficiency metrics, and how it will integrate into the existing IT infrastructure. Simultaneously, we start preparing the data — gathering, cleaning, and formatting it as needed. Well-defined requirements (BRD/FRD) and prepared datasets form the foundation for successful model development.

ML Solution Architecture Development

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SKALAR’s architects and ML engineers design a comprehensive solution: they select algorithms and modeling approaches, define data architecture (storages, data flows), and plan integration with other systems. They also choose the technology stack — from machine learning frameworks to the infrastructure for model deployment. In parallel, project documentation and system prototypes are created.

Implementation Planning

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We develop a detailed project plan covering all stages of ML development and implementation. The plan includes model-building iterations (Proof-of-Concept, prototype, final model), integration and testing stages, as well as staff training. Project roles are assigned (data engineers, ML developers, testers, etc.), and timelines and resources are defined for each phase.

Cost Estimation and ROI Assessment

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Based on the detailed requirements and plan, we calculate the project budget — a transparent cost estimate without hidden risks. In addition to costs, we help evaluate the expected return on investment (ROI) from ML implementation, so you understand when the project will pay off. This approach ensures confidence in the projects viability before development begins.

Model Development and Quality Control

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The team of data scientists and developers proceeds to create the solution. We build and train ML models while simultaneously developing the supporting environment (data, backend, interfaces). Regular quality tests are conducted: debugging models on test datasets and verifying system functionality. QA engineers monitor the stability of all components, and ML specialists refine the models until they meet target accuracy metrics.

Deployment, Training, and Support

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The finished ML solution is deployed into the production environment and integrated into your business processes. We train your staff to work with the new system if necessary — whether it’s using analytical dashboards or interpreting model results. After launch, SKALAR continues to provide technical support and development: monitoring model performance, updating algorithms as new data or business changes arise, and scaling the system to handle growing loads.

At each of these stages, the project is under the close supervision of SKALAR experts. This systematic approach to ML implementation ensures predictable results and minimizes risks. You can be confident — by entrusting us with your machine learning project, you gain a partner who manages the entire process "turnkey," from concept to stable results and increased business efficiency.

What ML solutions we create

Our experience covers machine learning projects across a variety of industries: retail, manufacturing, B2B services, HoReCa, and more. In each sector, ML helps solve specific tasks and delivers measurable benefits. Below are examples of ML solutions we develop for businesses in different industries:

Retail (E-commerce)

Machine learning algorithms detect fraudulent transactions, predict customer churn, and help retain customers in time. Data-driven solutions also enable customer segmentation based on behavior, personalized product recommendations, and optimized search results on websites. As a result, retail businesses see increased conversion rates and average order value, reduced fraud losses, and more precise marketing.

B2B Sector

ML systems are used to forecast product demand and optimize inventory, reducing warehouse costs. Algorithms help automatically calculate optimal pricing based on market factors, analyze credit or partner risks, and improve B2B logistics by building efficient delivery routes considering time and cost. All this allows B2B companies to make informed decisions faster and more effectively.

Manufacturing

In factories and plants, ML boosts efficiency through predictive analytics. Models predict equipment conditions using sensor data, enabling preventive maintenance and avoiding downtime. Computer vision systems automatically detect product defects on the assembly line, reducing reject rates. ML also optimizes the management of raw material and finished goods inventories by forecasting demand and preventing overstocking. The result is savings on repairs and waste, uninterrupted production cycles, and lower operational costs.

HoReCa (Restaurants and Delivery)

ML algorithms automate order and resource allocation. For example, the system decides in which kitchen and by which chefs a dish should be prepared, or how to optimally distribute orders among couriers based on distance, traffic, and delivery time. In case of disruptions (absent employee, broken vehicle), the model quickly rebuilds the plan to avoid delays. Additionally, restaurants use ML to assess staff performance, analyze customer reviews, and target promotions for loyal guests. These solutions increase service speed, customer satisfaction, and revenue growth through loyalty.

This is just a part of real scenarios where artificial intelligence brings value to businesses. Ultimately, any process involving data and a need for optimization is a candidate for improvement through machine learning. If you have a task you would like to solve with ML, we at SKALAR will find the right solution tailored to your industry and specific needs.

Why should you choose us

Top-Level Expertise in Data Science

Data analytics is our main strength. We thoroughly study client business challenges and select ML solutions based on numbers and deep industry understanding. You get not just a contractor, but an expert consultant who helps refine the product vision and formulate the correct requirements. This level of early-stage analysis ensures that the final solution fully meets expectations and effectively solves your business problem.

Experience in Implementing Complex AI Projects

SKALAR has extensive experience successfully delivering artificial intelligence and big data projects. Our portfolio includes systems involving more than 20,000 hours of development, which operate stably for our clients. We know how to design ML architecture for large data volumes, ensure corporate-level reliability and security. If your project requires scalability and fault tolerance, we have the necessary expertise.

Expert Team with 18+ Years of Experience

Your project will be handled by SKALAR’s key specialists — business analysts, system architects, ML engineers, senior developers, and project managers — each with 10–18 years of professional experience. In essence, your project is managed by IT industry veterans who have seen dozens of cases and technologies. This wealth of knowledge allows us to find optimal solutions where less experienced teams might struggle.

Unique Approach Combining Business and Innovation

We approach every project comprehensively — simultaneously considering technology, business value, and user convenience. Our method combines classical analysis methodologies (systems thinking, CRISP-DM for data) with flexible Agile development practices. This ensures a balance between detailed planning and adaptability to new data or market changes. As a result, projects are completed on time, within budget, and remain flexible to changes.

Full Turnkey Service Cycle

Our company covers all the needs of an ML project: data analytics and consulting, dataset collection and preparation, model development and training, software implementation, integration, staff training, support, and further development. You won’t need to hire separate specialists or contractors — SKALAR takes full responsibility for results at every stage. This saves your time and resources, ensuring high quality through a fully coordinated team.

The most frequently asked questions

What business tasks can be solved with machine learning?

How much historical data is needed for effective model training?

How long does it take to develop and implement an ML solution?

How much does an ML project cost?

How is an ML model integrated into existing systems and processes?

How do you check the quality and accuracy of models?

How secure is my data when using an ML solution?

Do you provide support after the ML solution is implemented?

If you have any further questions, our team will gladly provide guidance on all aspects of AI implementation in your business.

Technology Stack

Front-end

Back-end

DB and Analytics

Mobile

Deploy and Monitoring

Bootstrap

HTML 5

React.js

Figma

Modern Web App

d3.js

Redux

JavaScript

Web Sockets

Backbone.js

SCSS

CSS 3

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Ready to start developing a project?

Submit a request — and the SKALAR team will contact you to discuss the details and offer the best ML implementation strategy for your needs. We’ll help bring your idea to life with maximum efficiency and take your business to the next level through the power of artificial intelligence!

Examples where we have helped businesses realize the potential of artificial intelligence

A comprehensive automation system for dark kitchens designed to automate the receipt, preparation and delivery of orders. ZALP allows you to reduce staff and increase profitability by more than 15% and eliminate tardiness for 95% of orders, resulting in high customer returns

(View case)

20 000 hours

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