Case Studies

Case Studies, Cloud Architecture, Generative AI, machine learning & AI

Building a Smarter Project Management with AWS Powered Generative AI

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Effective project management turns great ideas into successful products. The startup Forsico.io aimed to improve this process. They created a project manager powered by Generative AI. Their platform automates tasks and tracks progress. As the user base expanded, however, new challenges arose. Forsico needed faster performance, better scalability, and more automation. This is where they decided to partner with Skyloop Cloud. We worked together to integrate AWS expertise into their system, helping them transform their vision into a reliable, high-performing platform. The primary goal was to make the AI more intelligent. The team adopted a method called Retrieval-Augmented Generation (RAG). This technique provides the AI with relevant context before it responds. AWS Lambda became the core of this workflow. It processed incoming data instantly. For the Generative AI itself, Amazon Bedrock supplied powerful language models. This ensured the generation of accurate and timely project updates. The architecture also included an important option. For very specific tasks, it could use a custom AI model. This hybrid design gave Forsico both flexibility and precise control. The system’s data storage and retrieval were also enhanced. Project documents are now held in Amazon S3 for security. The system then creates embeddings, which are numeric representations of text. A special Chroma database stores these embeddings. This process makes finding relevant information very fast. AWS Lambda functions retrieve only the necessary context. Then, they send it to the AI model for processing. As a result, the AI delivers highly precise and actionable responses. This efficiency improves output quality. It also controls costs by using resources carefully. The improvements delivered immediate and measurable results. Automated workflows replaced previous manual follow-ups, the Generative AI provided richer insights to the human project managers. Consequently, they could make quicker and more informed decisions. The platform’s serverless design allows it to scale automatically. It performs well during busy periods and also scales down to manage expenses during quiet times. Furthermore, the design integrates strong security. AWS services handle user authentication, permissions, and activity logging. This provides comprehensive protection for all users and their data. Forsico’s success demonstrates a powerful combination. It shows how AI innovation and cloud technology improve business operations. By working with an expert partner like Skyloop, they built a superior platform with the power of AWS. The project management tool is now smarter, faster, and more secure. This outcome provides a valuable model for other businesses. It shows how intelligent automation can increase productivity. It also supports scalability and strengthens decision-making across industries.

Case Studies, Generative AI, machine learning & AI

How Amazon Generative AI Power Beynex Technologies’ Real-Time Cognitive Analysis

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Detecting early signs of cognitive decline can change lives. Beynex Technologies is dedicated to this mission, offering Generative AI powered tools that sense decline in its earliest stages. Alongside detection, the platform promotes brain longevity through data-driven lifestyle guidance. As the platform’s user base expanded, new challenges arose. Faster insights, better scalability, and stronger security became critical. Partnering with Skyloop Cloud provided the expertise to solve these needs, combining healthcare goals with advanced AWS technology. The transformation began with a sharper focus on AI-driven analysis. Amazon Bedrock now powers models that interpret cognitive patterns and lifestyle data, spotting subtle changes before they become critical. This is accomplished using a service called AWS Lambda. Consequently, users receive feedback and suggestions without delay. The previous system had slower, batch-based processing. The new architecture also supports automatic scaling. This feature allows Beynex to handle sudden spikes in usage. It ensures the platform remains available without performance issues. Skyloop Cloud also re-engineered the data architecture for security and efficiency. User reports are now stored in a secure cloud environment, while structured data is organized in a managed database for analysis. It also uses load balancers for consistent traffic distribution. The system follows modern serverless practices to maximize the power of Generative AI. This approach minimizes operational overhead for the Beynex team. In addition, strong security measures protect sensitive health information. This careful design protects both the data and the user’s privacy, which is essential in healthcare. The benefits of this upgrade extend beyond simple performance. Faster data processing gives users more timely suggestions. This directly supports the goal of improving brain health outcomes. Automated scaling also reduces operational costs. The platform consumes resources only when needed. This makes the service more sustainable as it expands. Furthermore, enhanced security safeguards private health data. This builds crucial trust with both users and healthcare professionals. The combination of speed, cost efficiency, and security creates a strong foundation, while supporting new features and the future of Generative AI. Beynex’s journey demonstrates how cloud technology can amplify medical expertise. With Amazon Bedrock and secure AWS architecture, their platform is now faster, smarter, and more resilient. The demand for personalized healthcare continues to grow. Therefore, this approach serves as an excellent model for the industry. It shows how technology can amplify the impact of medical expertise. The result is meaningful benefits for patients, providers, and the entire healthcare system.

Case Studies, Internet of Things

Real-Time Wildfire Worker Safety and Health Monitoring System

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Overview A national forestry authority launched an advanced field solution to monitor the health, safety, and real-time location of personnel deployed in wildfire-prone areas. The project aimed to establish a robust and secure system that could operate in challenging outdoor environments and enhance emergency response capabilities. Problem Statement During wildfire incidents, the organization needed a reliable way to track the condition and location of field personnel. Existing solutions lacked integrated health monitoring, real-time geolocation, and reliable alerting mechanisms. The need was to design a portable, fault-tolerant system capable of collecting biometric and GPS data from wearable devices and delivering it to a centralized command center. Solution The solution integrated wearable health sensors and panic buttons with a LoRaWAN-based communication backbone. These devices transmitted encrypted data to AWS Greengrass compute devices deployed in the field, running components such as MQTT servers, stream managers, and API gateways. Local data was aggregated, visualized, and forwarded to the cloud using secure mTLS connections. The system architecture included: The user interface was accessible both at field and HQ levels via secured browsers, offering real-time health data, panic alerts, and positioning on a map. Outcomes The system was successfully tested under field conditions. Wearable devices reliably transmitted heart rate, body temperature, and emergency alerts. The monitoring panel accurately displayed worker states (e.g., normal, panic, fall detected), with alerts generated within seconds. During the test, devices simulated real emergencies (e.g., falls, panic button presses), and each event was correctly captured and displayed. The solution demonstrated high reliability, fast data transfer, and intuitive UI design that aided rapid decision-making. Lessons Learned The project underscored the importance of edge computing in latency-sensitive use cases. Operating without stable internet required offline-capable logic for on-site panels, which proved critical. Device pairing and management workflows were streamlined, and UI elements like colored alert statuses improved operational awareness. Future iterations will explore additional sensor integrations and automated drone responses to alerts.

Case Studies, Cloud Architecture, Cloud Security, Cloud Storage, DevOps, Internet of Things, machine learning & AI, Serverless, Software as a Service

IoT-Powered Forest Monitoring and Fire Prevention System

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Overview A government-affiliated environmental agency implemented an IoT-based forest monitoring system to enhance early fire detection and ecosystem tracking. The project aimed to establish a secure, scalable, and serverless infrastructure capable of collecting, processing, and visualizing real-time environmental data from deep forest areas. Problem Statement The agency faced challenges in monitoring large forested areas for potential fire hazards and environmental changes. Their existing system lacked automation, real-time responsiveness, and integration capabilities with modern cloud technologies. There was a critical need to build a reliable backend for processing sensor data and supporting both internal analysts and field users with a user-friendly dashboard. Solution The solution used AWS IoT Core to ingest data from a network of distributed environmental sensors that measured metrics such as CO₂ levels, temperature, and humidity. AWS IoT Core pushed the data to Amazon MQ for managed MQTT message queuing. From there, AWS Lambda processed the messages and performed lightweight analytics before storing the results in Amazon DocumentDB within a secure private subnet. On the front end, the application was hosted on AWS Amplify, accessed through an Application Load Balancer in a public subnet. This setup allowed field agents and administrative users to view dashboards, analytics, and alerts in real time. The entire solution was deployed inside an Amazon Virtual Private Cloud (VPC) for network isolation and security compliance. Outcomes The forest monitoring system significantly improved situational awareness across protected areas. Sensor data was now available in real-time, reducing fire response times and enabling data-driven resource planning. The agency reported improved operational efficiency and received positive feedback from both internal stakeholders and external partners. Lessons Learned One of the key takeaways was the importance of integrating serverless architecture and managed messaging services to reduce operational overhead. Additionally, placing the database in a private subnet enhanced security posture without compromising performance. The project also highlighted the need for automated alerting and visualization tools to improve response strategies during critical events.

Case Studies

Transforming a Legacy ERP Platform into a Modern Digital Workplace on AWS

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In today’s fast-paced digital landscape, organizations relying on legacy Enterprise Resource Planning (ERP) systems face significant challenges, including limited scalability, high maintenance costs, and difficulties in supporting remote workforces. Transitioning to a modern, cloud-based infrastructure is essential to address these issues and enhance operational efficiency. This article explores how integrating AWS End-User Computing (EUC) services can facilitate the transformation of legacy ERP platforms into robust digital workplaces.​ Challenges of Legacy ERP Systems Traditional ERP systems often operate on outdated hardware and software architectures, leading to several critical issues:​ Limited Remote Access: Providing secure and efficient remote access to on-premises ERP systems can be complex and costly. Scalability Constraints: Legacy systems may struggle to handle increased workloads, hindering business growth.​ High Maintenance Costs: Maintaining on-premises infrastructure requires significant investment in hardware, software licenses, and specialized personnel.​ Amazon WorkSpaces: A managed, secure Desktop-as-a-Service (DaaS) solution that enables users to access their desktops from anywhere. ​ Amazon AppStream 2.0: A fully managed application streaming service that allows users to securely access desktop applications from any device. ​ Transforming ERP with AWS EUC Services By leveraging AWS EUC services, organizations can modernize their ERP systems effectively:​ Case Study: Successful ERP Modernization Consider a manufacturing company that migrated its legacy ERP system to AWS using Amazon WorkSpaces. Conclusion Modernizing legacy ERP systems with AWS End-User Computing services offers a strategic pathway to creating a flexible, secure, and cost-effective digital workplace. Organizations can overcome the limitations of traditional ERP systems and position themselves for future growth by leveraging AWS’s scalable and reliable infrastructure.​

Case Studies, machine learning & AI, Serverless, Software as a Service

Upgrade your workforce with our GitHub COPILOT Offer

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GitHub Copilot is an AI-powered code completion tool developed by GitHub and OpenAI. It assists developers by suggesting code snippets and entire functions in real-time as they write code. Through machine learning, it understands the context of the code and provides intelligent suggestions. This accelerates the development process and enhances code quality. For businesses, GitHub Copilot can significantly boost productivity. It reduces the time developers spend on writing boilerplate code and helps maintain coding standards. By providing instant code suggestions, it allows teams to focus on solving complex problems rather than routine tasks. This leads to faster development cycles and more efficient use of resources. Moreover, GitHub Copilot supports multiple programming languages and frameworks. This versatility makes it a valuable tool for diverse development teams. It can aid in onboarding new developers by providing code examples and reducing the learning curve. Businesses can maintain consistency across projects as the AI suggests code that aligns with best practices. At Skyloop Cloud, we recognize the value GitHub Copilot brings to businesses. We offer interested companies a 10% discount when they acquire GitHub Copilot through us. Our team can assist in integrating Copilot into your development workflow and provide support to maximize its benefits. By partnering with us, you enhance your team’s productivity while reducing costs. GitHub Copilot is transforming the way developers write code. For businesses aiming to accelerate their development processes, it is a valuable tool. Skyloop Cloud is here to help you adopt GitHub Copilot effectively. Contact us to learn how you can utilize this AI-powered assistant and take advantage of our exclusive discount.

Case Studies

Transforming Educational Accessibility with Cloud Technology

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Project Overview AnonymizedApp, a sustainable education platform, is revolutionizing university students’ study processes by enabling the buying and selling of lecture notes. This project aimed to enhance the accessibility and usability of educational materials by translating lecture notes into multiple languages and providing related question-answering support. Project Summary The primary objective of this project was to translate lecture notes into various languages using Amazon Bedrock and provide comprehensive support by answering related questions. The process involved using Amazon Textract for OCR to read and extract text from PDF documents, followed by Amazon Bedrock to correct typographical errors and ensure accurate translations. The project also utilized Amazon Bedrock for answering questions about class notes, thereby supporting students in their learning process. Scope of Work The project leveraged a suite of AWS services to streamline the translation and question-answering processes for lecture notes. The workflow began with Amazon S3 as the storage solution for all submitted notes. AWS Lambda functions managed the initiation of the translation process, assessing document readability and triggering Amazon Textract for OCR when necessary. The extracted text was then processed in a containerized environment where Amazon Bedrock corrected typographical errors and translated the text into multiple languages. These translations were stored in a parameter store for easy access. Additionally, Amazon Bedrock was used to answer questions related to the lecture notes, enhancing the user experience through its advanced natural language processing capabilities. Challenges and Solutions One of the primary challenges faced during this project was ensuring the accuracy of text extraction from diverse document formats and qualities. Amazon Textract’s OCR capabilities played a crucial role in addressing this issue, but handling various languages and typographical errors required further refinement. Amazon Bedrock was instrumental in correcting these errors, ensuring that the translated texts were coherent and accurate. Another challenge was the integration of multiple AWS services into a seamless workflow. Coordinating Amazon S3, AWS Lambda, Amazon Textract, and Amazon Bedrock required meticulous planning and testing to ensure smooth interoperability. By deploying containerized environments and leveraging AWS Lambda for orchestration, we were able to create a robust system that managed these interactions efficiently. Lessons Learned Conclusion This project successfully transformed the accessibility and usability of educational materials for AnonymizedApp by leveraging advanced AWS technologies. The integrated approach ensured that all lecture notes, regardless of format or readability, were accurately processed, translated, and made available for both review and interactive use. The combination of Amazon S3, Textract, Lambda, and Bedrock created a robust system that significantly enhanced the learning experience for students across different languages. By overcoming challenges and implementing effective solutions, we delivered a system that not only met but exceeded the project’s objectives, setting a new standard for educational accessibility in the digital age.

Case Studies

Modernizing Document Processing with AWS for Anadolu Ajansı

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​Modernizing document processing is a critical step for organizations aiming to enhance efficiency, reduce operational costs, and support a flexible digital workplace. By integrating AWS End-User Computing (EUC) services with advanced document processing solutions, businesses can provide secure, scalable, and accessible environments for their workforce.​ Organizations often grapple with manual document processing methods that are time-consuming, error-prone, and resource-intensive. These challenges can lead to compliance issues, operational delays, and increased costs. Additionally, the lack of remote access to document processing tools hampers productivity in today’s increasingly remote work environments.​ AWS Services for Enhanced Document Processing To address these challenges, AWS offers a suite of services that automate and streamline document processing:​ Integrating EUC Services for a Modern Digital Workplace Incorporating AWS EUC services further enhances the document processing workflow:​ Case Study: Anadolu Ajansı’s Transformation Anadolu Ajansı, Turkey’s leading news agency, embarked on a journey to modernize their document processing methods using AWS’s advanced AI technologies and EUC services. The goal was to enhance efficiency, reduce processing time by 30%, and cut operational costs by 20%. The project leveraged a suite of AWS services, including Amazon Textract, Comprehend, Lambda, WorkSpaces, and AppStream 2.0, to create a scalable, high-availability, and automated document processing workflow. By integrating EUC services, Anadolu Ajansı provided its employees with secure, remote access to document processing tools, enabling a flexible and efficient digital workplace.​ Conclusion By integrating AWS’s document processing solutions with EUC services, organizations can automate their workflows, enhance data accuracy, and provide secure, remote access to essential applications. This holistic approach not only streamlines operations but also supports a modern, flexible digital workplace, positioning organizations for success in the evolving business landscape.

Case Studies

Cloud Transition Strategy for a Customer

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This project aimed to efficiently connect service providers with those in need. The platform placed a high priority on service requests by considering accumulated points, highlighting the importance of providing quality service in the long run. With the goal of improving user experience, Helpers decided to transition its current infrastructure to the cloud. This involved implementing a comprehensive solution that seamlessly combined various tools and services such as Bitbucket, AWS CodeBuild, AWS ECR, ECS, RDS, IAM, and CloudWatch. Regarding the Customer This project allowed service providers and seekers to engage effortlessly. The platform optimized service prioritization by awarding points for services rendered. The standout aspect was the ability to establish service prices in advance, removing the need for the conventional bidding system and giving users direct access to verified helper accounts. Summary of the Project The project required a thorough architectural revamp. The migration involved setting up a pipeline to transfer code from Bitbucket to AWS ECR using AWS CodeBuild. The application was deployed on an ECS container, and RDS was placed in a private subnet. Additionally, an Application Load Balancer was implemented to direct traffic on port 443. Users and roles were carefully created, and CloudWatch was used for effective monitoring of the cloud infrastructure. Technical Project Plan – Scope of Work Building Infrastructure Using Terraform During the migration, the cloud infrastructure was carefully designed using Terraform. This guaranteed an environment that could easily grow, was well-managed, and could be replicated. CodeBuild efficiently handled the pipeline between Bitbucket and AWS ECR, ensuring seamless transitions. ECS Container Deployment ECS was used to execute tasks for web and mobile applications. The ECR repository stored the application image, which was then pulled by the ECS cluster to execute tasks. For optimal resource allocation, the cluster was equipped with an Application Load Balancer that listened on port 44329 and redirected to port 443. DNS Management and ACM Integration The DNS name of the Application Load Balancer was registered with a DNS provider as a CNAME, allowing for the convenient utilization of a sub-domain. The web application’s frontend was hosted using the Application Load Balancer DNS name and was secured with an ACM certificate for sub-domains. RDS and EC2 Bastion Host The database backend was powered by RDS, which ran Microsoft SQL Server Standard Edition in a private subnet. Containerized applications sent queries through an EC2 instance, which was deployed as a bastion host to ensure secure and controlled access to the database. Cloud Monitoring using CloudWatch CloudWatch was utilized to monitor the cloud infrastructure and ensure the reliability and performance of each service. The ECS cluster gained significant advantages from container insights, offering comprehensive monitoring capabilities. IAM users and roles followed the principle of least privilege, which helped to strengthen security measures. In Conclusion The migration to a cloud-native architecture demonstrated a strong dedication to delivering a dependable and scalable service platform. Optimizing resource utilization, enhancing security, and ensuring a seamless experience for both service providers and seekers was a key aspect of this solution. This transformation showcased Helpers as a platform that was always looking ahead, prepared to adapt to the changing needs of its users.

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