Oles Riaboi
Senior .NET Developer | ASP.NET Core | Blazor | Desktop
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European Transport Compliance & Rules Engine
As part of a large enterprise ERP system, I developed a specialized rules engine for transportation operations across the European Union.
The module automatically determines whether a planned transportation operation complies with applicable regulations, taking into account vehicle characteristics, cargo parameters, routes, countries involved, and specific transportation conditions.
The underlying business logic was highly complex, with numerous regulatory rules, exceptions, dependencies, and combinations of operational parameters. Many of these requirements had previously been handled manually, making the process difficult to maintain and creating a significant risk of human error.
The solution transformed this complex regulatory knowledge into an automated software-based decision engine. It helped transportation teams identify potentially non-compliant operations before they were executed, significantly reducing the risk of incorrect transportation practices, regulatory violations, and costly fines.
The system was implemented as part of a proprietary ERP platform using .NET, MSSQL, WinForms and DevExpress. Due to the confidential nature of the ERP system, the source code and detailed business rules cannot be publicly disclosed.
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Automated Transportation Pricing Engine
As part of a large enterprise ERP system, I developed an automated engine for calculating the cost of transportation orders based on more than 40 business and operational parameters.
The pricing calculation involved complex business rules, multiple dependencies between parameters, and a large number of possible combinations. Previously, the calculation was performed using Excel-based processes and could take up to 10 minutes for a single order.
I designed and implemented a software-based calculation engine that automated this process and moved the complex pricing logic from manual Excel calculations into a maintainable and executable business rules system.
The result reduced the calculation time from up to 10 minutes to approximately 5 seconds per order.
Because the solution was developed as part of a proprietary ERP system, the source code and implementation details cannot be publicly disclosed. However, I can discuss the architecture, engineering challenges, optimization approach, and the business logic involved at a high level.
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Caffeo (https://caffeo.uno/)was created to solve a problem I experienced firsthand: running a growing network of cafés with fragmented tools, spreadsheets, and systems that were not designed around the realities of day-to-day café operations.
Instead of adapting the business to existing software, I built Caffeo around the actual workflows of café owners, managers, and baristas.
The platform combines sales, inventory, product costs, employee shifts and payroll, financial reporting, and real-time analytics in a single system. It includes an Android application for point-of-sale operations and a web-based management platform for owners and managers.
A key requirement was reliability in real-world conditions. The POS application can continue operating without an internet connection and automatically synchronizes data when connectivity is restored.
Caffeo evolved from software used to operate a real café network into a multi-location SaaS platform, designed to give owners a clear view of revenue, costs, inventory, profitability, and day-to-day operations without the complexity of traditional enterprise systems.
The product was built through years of direct experience with the business it manages, which allowed the software to evolve around real operational problems rather than hypothetical requirements.
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Nutrexia was created to make nutrition tracking simpler, faster, and more intelligent.
Traditional calorie tracking requires users to search for foods, enter portions manually, and maintain their food diary throughout the day. Nutrexia was designed to remove much of this friction by using AI to turn a simple photo of a meal into nutritional information.
The app combines AI-powered food recognition, calorie and macro tracking, personalized nutrition goals, recommendations, and dietary analytics in a single mobile experience.
Users can photograph their meals, get an AI-based estimate of calories and macronutrients, track their progress over time, and receive personalized recommendations based on their eating habits.
The goal was not simply to build another calorie counter, but to create a personal AI nutrition assistant that makes consistent food tracking practical for everyday use.
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