Our team has successfully partnered with HAULK, a leading freight operator in North America, for a long time. Together, we have built a unique digital ecosystem for logistics management, encompassing fleet management, technical support, and partner collaboration. The client has now decided to leverage their experience and established infrastructure to expand into a new business sector. They approached us with a request to develop a full-fledged online store for auto parts and equipment. Our goals were to: Implement a sales system for parts from the company's warehouses, utilizing a fully updated back-office infrastructure for the BodyShop auto repair service. Develop a web platform for ordering and purchasing parts, catering to both B2B and B2C customers. Integrate the website with the back-office platform to manage truck maintenance, inventory, pricing, offers, reporting, and analytics. Implement a checkout system to provide personalized pricing for customers and integrate payment services. The project enabled the company to capitalize on its expertise, infrastructure, and competitive advantages to enter a new business domain, auto parts retail. The implementation of the portal delivered the following business benefits: 100% coverage of HAULK's audience with a modern online sales service. A personalized eCommerce experience for all customer segments, which was previously impossible. Early adopters of the new platform were long-time partners who had been using the company's services and support for years. At the same time, the store attracted a new, relevant audience from the freight transportation industry. The development of this unique online store became a significant eCommerce case for the WEZOM team in the highly competitive U.S. market. However, we are confident that the demand for such solutions will continue to grow.
WEZOM
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WEZOM Portfolios
STVOL is one of the leaders in civilian gun sales in Ukraine. Today, the retailer officially represents over 100 global gun brands and offers one of the largest shooting ranges in Europe. Our team has been working with STVOL for 4 years. We have implemented three eCommerce projects for them, with continuous improvements to the user experience on the client's online store. We built a unique eCommerce platform to meet the client's non-standard business needs. The user flow in the client's market niche is significantly different from classic eCommerce, as the sale of civilian guns is strictly regulated. Moreover, the portal needed to adequately present the benefits of the STVOL brand and its projects, such as a shooting range and the range of goods offered. All of this had to be considered when developing the new version of the website. The new website is seamlessly integrated into the STVOL digital ecosystem: it is connected with CRM, inventory management systems, accounting software, marketing tools, analytics platforms, payment services, logistics services, etc. For the personalized recommendation system, the team implemented a neural network with a hybrid approach to filtering and integrated it into the store via API. The model constantly self-learns and improves. The team's efforts to simplify the user flow, improve the search experience, and digitalize the loyalty program helped attract a new audience and increase the engagement of existing customers. Now, 100% of customers use a single loyalty system, combining both online and offline bonuses. The new system of individual recommendations, based on AI, has significantly improved the dynamics of repeat purchases and conversion. Our cooperation with STVOL is one of the best examples of the benefits of custom eCommerce development for unique business needs. It has provided the store with unlimited opportunities for scaling, functional development, and discovering new success formulas.
A client approached WEZOM to create a digital B2B web portal for rehabilitation equipment. The new solution had to be a more convenient alternative to the former messenger where the order process had taken place before. Specifically, the client had to receive full automation of this process to reduce the manual work the client's managers usually perform. The project aimed to optimize the order process by creating an intuitive product catalog integrated with an accounting and CRM system. That allowed the team to work iteratively to quickly adapt to changing client requirements. Thanks to this, all changes were introduced instantly, which was especially important during the integration with OpenERP and CRM systems. The team included frontend and backend developers, designers, and a project manager to maintain constant communication with the client. The new portal significantly enhanced order efficiency and reduced the workload on managers. With a newly created, tailored B2B portal for medical rehabilitation equipment, the client company fully automated the order processing system. On top of that, the integration with OpenERP and CRM platforms allowed the company to digitize its document workflow and inventory management operations.
A client approached us with a startup idea: to develop a user-friendly app for monitoring nutrition that helps users improve their diet and break bad habits. The available calorie and nutrition trackers on the market were too limited for this purpose. The launch of NutriTrack on the App Store was a huge success. In just a month and a half since the release, the app has been downloaded by over 50,000 users. The Android version will be launched soon, which will further accelerate the app's audience growth. In the meantime, the development team is already working on the first major update for the system. Very soon, NutriTrack will feature a fully integrated AI chatbot that would consult users through live conversations.
WEZOM was approached by EZ Blockchain, a leading U.S. cloud mining provider working with energy, oil, and gas companies. Their goal was to digitize mining operations, boost transparency, automate workflows, and enhance customer experience—key steps to enter the B2C market and scale with confidence. Objective Create a secure, scalable, and user-centric dashboard that enables real-time monitoring of mining equipment, automated billing, transaction history, and a robust admin back-office. Our Approach Live Monitoring: The dashboard displays real-time data (temperature, power use, hash rate, failure rate) via WebSocket. Clients gain full visibility and control, without needing to contact managers. Adaptive UX/UI: We designed a responsive, intuitive interface for both desktop and mobile. UX research helped us ensure usability for real users across devices. Billing Automation: Invoices, transactions, and contracts are auto-generated and stored in one place. API integration with accounting tools enabled fast reporting and simplified payments. Admin Panel: Built on a modular architecture, the back office allows real-time customer and rig management, analytics, logging, and custom reporting. The Client Success module supports proactive customer care. Results The MVP launch exceeded expectations: Customer loyalty index grew by 60% New customer lead scores averaged 8/10 Full transition to digital processes and self-service model EZ Blockchain now delivers a streamlined, transparent experience to both clients and staff—reducing manual work, improving decisions, and enabling real-time control of mining operations. The platform is set for expansion with enhanced features and a redesigned UI in the next phase.
Our client is an independent analytical center monitoring public sentiment on the war in Ukraine via X (formerly Twitter). To improve speed and accuracy, WEZOM was tasked with building a fully automated AI solution for classifying English-language tweets by topic and sentiment in real time. Objective Develop a scalable platform that collects tweets via API, filters bots and irrelevant content, performs emotional and thematic classification using NLP, and visualizes results through a GDPR-compliant dashboard—enhancing decision-making and reducing manual analyst workload. Solution & Technology Model architecture: We used BART-large-MNLI for topic classification and DeBERTa-v3 for sentiment analysis, fine-tuned on 3,000+ manually labeled tweets across hashtags like #ukrainewar, #nato, #refugees. Pipeline: Built in Python using HuggingFace, Pandas, and Scikit-learn, with validation metrics including Precision, Recall, and F1-score. Filtering: Tweets were cleaned of spam, bots, and non-English content; only text and hashed user IDs were stored to meet GDPR standards. Dashboard: A real-time interface lets analysts track sentiment trends and topic distributions across thousands of daily tweets. Results The client now performs instant analysis of tens of thousands of tweets, improving the accuracy and transparency of insights on global public perception. The system is already used by the client’s analytics department and has proven scalable and adaptable to other platforms like Reddit or Telegram. Its bot filtering and war-topic tuning make it a powerful tool for media and policy analysts alike.
A large international logistics company turned to WEZOM to improve route efficiency, reduce fleet wear, and enhance safety. Their goal was to implement a GPS and accelerometer-based platform to analyze truck sensor data, without relying on costly streaming infrastructure or complex DBMS solutions. Objective Create a telematics platform that processes data from Teltonika FMB920 trackers, identifies key events (idling, speeding, deviations), detects aggressive driving, and integrates seamlessly with ERP/TMS systems—all while enabling analytics in Excel via flat files. Our Approach Data Collection & Standardization: Using Teltonika FMB920 devices, data is gathered via GPS, accelerometer, and CAN bus. It’s transmitted using Teltonika’s protocol and converted into a standardized JSON format. Event Detection: We built algorithms to detect route deviations, idle periods, speeding, and aggressive driving using GPS and sensor data. Support for ADAS events like collision warnings and lane departures enhances road safety. Batch Processing: The system operates in batch mode, exporting data into Excel files for simplified analysis. This eliminates the need for heavy database infrastructure. Integration & Sync: REST API and MQTT broker ensure full synchronization with ERP/TMS/WMS systems and third-party platforms, enabling seamless access to telematics data and real-time alerts. Results Unified analysis of data from hundreds of trucks Driver behavior and route anomalies are automatically detected Full Excel-based analytics with no server maintenance Smooth integration with corporate systems via API and MQTT With SafeRoute AI, the client now benefits from enhanced control over fleet performance, safer transportation, and simplified telematics management, without the cost of complex infrastructure.
A government defense agency approached WEZOM to automate the analysis of aerial reconnaissance images. Manual identification of strategic objects took days and limited decision-making speed. The goal was to build an AI-powered solution to detect and classify key military and infrastructure objects in satellite and drone imagery. Objective Develop a high-performance object detection system using computer vision to analyze large datasets, reduce manual labor, and enable near real-time classification of critical objects like aircraft, refineries, ports, and military vehicles. Our Approach Model Architecture: Built on YOLOv8 and PyTorch, with transfer learning using the DOTA dataset for remote sensing. We adapted the model for specific target classes and enabled support for oriented bounding boxes (OBB) to recognize objects at angles. Processing Pipeline: Implemented batch inference with FastAPI for real-time access. OpenCV handled image preprocessing and visualization, while results were stored in PostgreSQL and formatted in JSON for integration with GIS and dashboards. Accuracy & Speed: The model achieved mAP@0.5 = 80%, precision = 0.82, and recall = 0.80. Inference speed was ~0.3s per image, allowing scalable analysis of thousands of images with over 200,000 labeled objects. Results The solution automated aerial image analysis, minimized human error, and allowed integration with defense and intelligence systems. It supports active learning, is ready for new object classes, and can be scaled to GPU clusters for high-volume processing. Next Steps Ongoing work includes active learning integration, GIS platform connectivity, and expansion of object categories to adapt to dynamic reconnaissance needs in both civilian and military scenarios.