Acropolium modernized a legacy data integration platform into a high-performance API data pipeline for BI and reporting. The client, operating a statistical data portal, needed scalable ingestion from 16+ APIs, reliable transformation, and structured delivery to analytics teams. We redesigned the system with NestJS, Redis, PostgreSQL, and React.js, introducing event-driven architecture, multithreaded processing, and standardized adapter development. This enabled 10× faster performance, reduced integration time to one month, and ensured consistent, high-quality datasets for BI. The platform now serves as a centralized enterprise data integration hub, supporting continuous data ingestion, advanced analytics, and future expansion.
Acropolium
Custom Software Development
Acropolium
3.7★★★★0 reviews8 Portfolio
Acropolium Portfolios
Acropolium rebuilt a legacy listing management system into a scalable SaaS platform for merchants operating across Amazon and eBay. The solution introduced a Node.js backend, React.js frontend, and microservices on AWS ECS, ensuring stability, performance, and seamless marketplace integrations. The platform now supports bi-directional inventory synchronization, centralized catalog control, and reliable API updates, enabling merchants to manage listings efficiently and scale operations without performance loss.
Acropolium developed a unified ERP ecosystem to replace fragmented legacy systems and support complex maritime logistics operations. The platform consolidates booking, container management, voyage planning, financial reconciliation, and customer self-service into a single digital environment. Built with Node.js, .NET, React.js, React Native, and AWS cloud-first architecture, it integrates external systems (banking, customs, GPS, IoT devices) and supports multi-language, multi-currency operations.
Acropolium modernized a legacy hotel management system into a cloud-native SaaS platform with an AI concierge, cutting IT overhead by 25% and boosting guest satisfaction by 15%. The project unified multi-location operations, automated workflows, and enabled centralized reporting for a growing hotel group in Italy.
Acropolium modernized a legacy Warehouse Management System (WMS) into a scalable big data platform, boosting forecast accuracy by 30% and cutting inventory costs by 20%. The project enabled real-time dashboards, predictive analytics, and compliance-ready data pipelines for a logistics provider in Denmark.
Our event management client needed a live-streaming app for seamless global event coordination and broadcasting. To upgrade services, they opted for SaaS-based video streaming, emphasizing on robust payment systems. The goal was to revitalize platform components for better performance, scalability, and user satisfaction. Solution We crafted a strategy covering business objectives, target demographics, revenue models, and feature integration. Flexible monetization options were adopted to enhance revenue and ROI. Addressing system challenges, we chose a suitable tech stack for modernization. Agile methodology facilitated adaptability throughout development, during which we: Implemented scalable architecture and optimized performance rigorously to ensure the platform can manage growing loads and deliver a seamless experience during peak usage. Employed strong security measures, such as encryption protocols and secure access controls, to protect the platform against cyber threats and safeguard user data. Seamlessly integrated secure payment gateways, merchant services, and financial APIs to enable smooth transactions, billing, invoicing, and revenue tracking for event organizers and attendees. Added interactive features like live chat, polls, Q&As, and social media integration to boost audience engagement and foster real-time interaction during live events. Results The client received a highly functional, scalable platform with the following tangible benefits: The client's users can now effortlessly host and live-stream offline events to a global audience, with streaming lags reduced by 95%. With this solution, users can achieve global streaming with an end-to-end latency of two seconds or less. The SaaS platform dynamically scales during peak viewing hours, employing increased elasticity and intelligent load balancing to seamlessly accommodate its viewers. Read our case study here: https://acropolium.com/portfolio/saas-based-live-event-streaming-platform-development/
Custom Supply Chain Analysis Tool Development Request A leading logistics company sought to optimize operations and meet diverse customer demands like expedited shipping and just-in-time delivery. They wanted to employ data analytics to tailor services, enhance customer satisfaction, and improve efficiency through advanced inventory management and optimized transport routes. Our software development team was tasked to create a robust supply chain analytics solution to leverage big data effectively. Solution We used predictive analytics and machine learning to analyze historical and real-time supply chain data, improving demand forecasting and inventory management accuracy. To tackle data fragmentation, we implemented advanced big data integration methods. This included developing efficient data pipelines to consolidate information from various sources into a centralized repository, ensuring data consistency, and providing a comprehensive view of the supply chain ecosystem. We provided intuitive dashboards and reports for real-time supply chain performance monitoring and informed decision-making. Robust data security measures, including encryption protocols and access controls, ensured compliance with GDPR and protected confidential information. Our developers delivered software with advanced analytics for historical and real-time data. We also provided a centralized repository managing supply chain data from diverse sources. Lastly, we designed user-friendly dashboards and reports for easy data access and decision-making. Results -Our team developed a supply chain analytics platform that enhanced system availability and increased customer retention: -System downtime was reduced by 20%. -Operational efficiency rose by 27%. -Inventory costs dropped by 15% through optimized management practices. -Customer retention rates climbed by 22% due to personalized engagement and enhanced service quality. Read our case study here: https://acropolium.com/portfolio/supply-chain-data-analytics-software/
Custom Data Profiling and Quality Monitoring Soft An investment tools provider requested a robust monitoring tool and software to ensure quality. The set of data quality solutions had to be tailored to handle extensive volumes of sensitive data with real-time monitoring features. We were asked to leverage AI-driven methods to enhance data quality, facilitating dependable data collection and generating insights with automated entry procedures. Solution Our developers established standardized processes and frameworks for real-time data quality monitoring, employing dashboards to track issues and automated alerts for immediate notifications. The system's design ensures scalability, with the data profiling and quality tool structured for horizontal scalability. • Leveraging advanced machine learning algorithms, we ensured the data profiling tool autonomously analyzes incoming data, identifying types, patterns, and anomalies. • By integrating real-time data quality monitoring capabilities, we promptly flag and address data quality issues as they emerge. • The live quality check feature also contributed to the propagation of inaccuracies within the system through proactive ML-driven interventions. • Within our scalable data quality tool and profiling software architecture, customizable features empower clients to adjust detection settings for incorrect data. Results Acropolium developed an all-encompassing data monitoring and profiling solution, where ML-powered features translated to cost savings and improved efficiency: • The client now successfully processes and ingests up to 30 terabytes of data daily, showcasing a 200% improvement in scalability. • Real-time monitoring reduced the time to identify and address data quality issues to less than 1 hour. • Data errors and inconsistencies decreased by 40%, leading to a final data quality rate of 95%. • Data processing time decreased by 30%, averaging 8 hours compared to the initial 12 hours per 1 terabyte dataset.