Artificial Intelligence AI-Powered Fraud Detection for Financial Institutions Client Background: A prominent financial institution serving millions of customers globally, the client offers a wide range of banking services, including personal and business accounts, loans, and investment products. As a major player in the industry, the client faced increasing threats from financial fraud, which posed risks to both customer trust and operational security. With a growing customer base and evolving fraud tactics, the institution needed an advanced solution to detect and prevent fraudulent activities in real-time. Challenges: The client faced several significant challenges in managing fraud detection. Increase in complex fraud attempts were becoming difficult to identify using traditional methods, putting the institution at risk of substantial financial losses and reputational damage. The existing fraud detection systems were slow, often flagging legitimate transactions as suspicious, which led to delays and frustrated customers. Manual review processes were added to the issue, requiring a large workforce to monitor suspicious activities, resulting in high operational costs. As fraud tactics evolved, the institution’s outdated systems struggled to keep up with new schemes, leaving vulnerabilities in their detection capabilities. Delays in handling fraud cases also caused customer discontent and damaged trust, which made it more difficult for the organization to keep customers loyal. Our Solutions: We implemented an AI-based fraud detection system that utilized machine learning algorithms to detect and prevent fraudulent transactions in real-time. This advanced system continuously improved its ability to identify fraudulent activities, enhancing both security and operational efficiency. Real-Time Fraud Detection: The AI system analyzes transaction patterns in real time, identifying potential fraud attempts instantly and reducing the time between detection and intervention. This proactive approach minimized the window for fraudulent transactions to impact the client. Advanced Machine Learning Algorithms: The platform utilized machine learning to adapt and improve fraud detection accuracy over time, learning from historical data and new fraud patterns. This allowed the system to refine its predictions and offer increasingly accurate insights into potential fraud. Risk Scoring System: The solution assigned risk scores to transactions based on multiple variables, helping the institution prioritize suspicious activities for investigation. This ensured that the most critical cases were handled promptly, reducing the overall risk to the business. Automated Alerts and Actionable Insights: The system provided automated alerts to the fraud detection team, along with actionable insights to take swift action, preventing losses. These insights were designed to guide the team in making informed decisions about potential fraud scenarios. Seamless Integration with Existing Systems: The AI-based system was designed to integrate smoothly with the client’s existing infrastructure, ensuring minimal disruption to operations. This allowed the client to enhance their fraud detection capabilities without changing their entire system. Outcomes: The AI-based fraud detection solution significantly enhanced the client’s ability to prevent financial fraud and streamline operations. By reducing manual intervention and automating key processes, the system boosted the client’s overall efficiency and security. Faster Fraud Detection: Fraudulent transactions were identified and flagged in real-time, reducing detection times and minimizing financial losses. The speed of detection allowed for quicker intervention and reduced the window for fraud to cause significant damage. Increased Detection Accuracy: The AI system’s continuous learning capabilities improved fraud detection accuracy, significantly reducing false positives and minimizing customer disruption. This resulted in a more efficient fraud management process, as agents could focus on higher-risk transactions. Lower Operational Costs: Automation of the fraud detection process reduced the need for manual intervention, cutting down operational costs and freeing up resources for other tasks. This allowed the client to allocate their resources more effectively and increase overall productivity. Improved Customer Trust: Quicker and more accurate fraud detection enhanced customer confidence in the institution’s security measures, leading to increased satisfaction. Customers felt reassured knowing their financial data was being closely monitored and protected in real-time. Scalability and Adaptability: The AI solution easily adapted to new fraud patterns and scaled with the growing customer base, ensuring future-proof fraud protection. As the client’s needs evolved, the system continued to provide the flexibility required to handle increasing transaction volumes and advanced fraud tactics.

Data Engineering Solving Automotive Data Challenges with Scalable Telemetry Systems Client Background: With a focus on connected vehicles, a major global automobile manufacturer runs a sizable fleet of millions of vehicles installed with Internet of Things sensors that gather and send real-time telemetry data. By tracking everything from safety features to vehicle performance, these sensors provide insightful information that improves both driving safety and enjoyment. The organization, which is dedicated to being at the forefront of innovation, aimed to enhance its capacity to handle and analyze the enormous amounts of data produced every day, guaranteeing scalability and effectiveness as its data requirements increase. Challenges: Data overload and storage management were the company's biggest problems. The massive amount of telemetry data produced by millions of connected vehicles easily overwhelmed the capability of the storage infrastructure that was in place, which was not scalable enough to effectively manage present and future data expansion without impairing system performance. Along with data overload, the organization also had to deal with uneven data quality because of chaotic, vague, or incomplete telemetry data from IoT devices, which made it difficult to get reliable insights for safety and optimization. Latency delayed critical decisions, while scattered data across systems complicated integration. The company also needed scalable infrastructure to support fleet growth. Our Solutions: We developed a cloud-native, scalable telemetry system designed to handle dynamic workloads and future data growth needs. Scalable Cloud Storage Architecture : Developed a flexible cloud-based storage system capable of handling large-scale data volumes while ensuring efficient data retrieval and scalable storage, supporting future growth as the company’s fleet expands. Real-Time Telemetry Data Cleansing System: Created an automated pipeline to clean, filter, and validate telemetry data in real time, ensuring that only high-quality data is processed for accurate insights and analysis. Centralized Data Integration Engine : Built a unified platform to gather and synchronize data from various sources, creating a centralized data lake that facilitates smooth access for analysis, reporting, and decision-making across departments. Scalable Infrastructure for Machine Learning Deployment: Constructed a flexible and scalable infrastructure for deploying advanced machine learning models, enabling predictive analytics that offer deeper insights into vehicle performance and maintenance needs. Outcomes: The automotive giant successfully transitioned to a scalable telemetry system, enabling real-time analytics and future-proofing its data infrastructure. Enhanced Data Storage & Efficiency: The cloud-based solution optimized data storage, offering both scalability and efficiency, while preparing the system to accommodate future data expansion without performance degradation. Elevated Data Quality & Consistency: The real-time data cleansing pipeline ensured that only accurate, high-quality data was used for analysis, improving decision-making and supporting reliable insights into vehicle safety and performance. I nstantaneous Insights with Minimal Latency: By implementing a low-latency stream processing system, the company can now act on telemetry data instantly, significantly enhancing responsiveness, especially in safety-critical scenarios. Future-Ready Data & ML Framework: The scalable infrastructure lays a strong foundation for future data growth, ensuring that the company remains equipped to drive innovation in predictive analytics and machine learning applications for vehicle performance optimization.

Cloud AI/ML Enhancing Model Training Pipelines for Vision Applications in Retail Client Background: Our customer is a well-known retailer specializing in various consumer items, including clothing, electronics, and household necessities. With its expanding online platform and physical shops, the company serves millions of clients globally. To remain competitive and provide outstanding client experiences, they have integrated the latest technologies into their operations as part of their business plan. Despite having a strong digital infrastructure, the company struggled to scale its computer vision models for tasks like inventory management, shelf scanning, and personalized recommendations. Their existing vision solutions were underperforming, causing delays and inefficiencies, which prompted them to seek a solution from Regami Solutions. Challenges: The main issue facing the client was the slowness and inefficiency of their pipeline for training computer vision models, which could not keep up with the increasing amount of data produced by their online platforms and retail locations. Time-consuming manual labeling and inadequate, scalable model designs were features of the existing system. Their capacity to swiftly implement new models was impeded, which affected inventory management and product identification. The business also had significant item detection mistake rates, which resulted in lost or misplaced products, stockouts, and unhappy customers. Updating inventory records was delayed since their current structure was inflexible enough to include real-time data. The client approached us to build a scalable, and efficient pipeline that would enhance their model training process and deliver improved accuracy and faster deployment cycles. Our Solutions: Our approach incorporated the latest advancements in AI and machine learning, as well as a deep understanding of the unique needs of retail-based vision applications. Here’s how we tackled the challenges: Automated Data Labeling and Augmentation: We implemented an automated data labeling system using active learning to reduce human errors and speed up the process. Data augmentation techniques like color adjustments and scaling improved dataset variety and model accuracy. Model Architecture Designed for Faster Processing: We improved model architecture with parallel processing and lightweight models like MobileNet to improve performance and reduce computational load. Scalable Cloud-Based Training Infrastructure: A scalable cloud infrastructure was built to efficiently handle vast retail data, supporting distributed training for quick data processing. Adaptive Learning and Continuous Model Monitoring: A continuous monitoring system was implemented for immediate model performance tracking, enabling quick adjustments and adaptive learning for sustained accuracy. Integration with Retail Management Systems: We integrated computer vision models with retail management systems for automated inventory tracking, product recognition, and immediate data updates across stores and warehouses. Custom Training Pipelines Specific to Retail Needs: Custom training pipelines were designed for various retail products, supporting different data types and camera setups for efficient model training. Outcomes: The enhanced training pipelines not only enhanced the performance of their computer vision models but also helped the company achieve operational efficiency and improve the customer experience. Here are the key outcomes: Faster Model Training Cycles: The new pipeline significantly sped up model training, allowing quicker updates and more frequent experimentation. This agility helped the client stay ahead of evolving retail demands. Improved Object Detection and Recognition: Enhanced models produced more accurate product recognition, reducing misidentifications and enhancing inventory management, leading to fewer operational errors. Scalability to Handle Growing Data Needs: The cloud-based infrastructure now supports seamless scalability, easily handling increased data and expanding product lines as the client’s business grows. Enhanced Real-Time Decision Making: Real-time inventory tracking and automated updates allowed for quicker stock replenishment, streamlining the supply chain and improving decision-making. Reduced Operational Costs: Automating manual tasks like data labeling and product tracking resulted in reduced operational costs, improving productivity and enabling staff to focus on higher-value tasks. Better Customer Experience and Increased Sales: Accurate inventory and product recognition improved the shopping experience, improving customer satisfaction and causing increased sales.

Cloud Engineering Designing Resilient Cloud Architectures for Financial Services Client Background: With over 15 million clients, our client is a global financial services company operating in the banking, insurance, and investment management industries. Modernizing infrastructure is essential for financial services to stay competitive, comply with regulations, and manage growing sensitive data. To guarantee scalability, security, and compliance as part of their digital transition, they had to update their infrastructure. Transitioning to cloud-based solutions enhances security, efficiency, and scalability but challenges availability, disaster recovery, and regulatory compliance. So, they opted for expert assistance to create an efficient cloud architecture that would withstand critical tasks while ensuring data security and high availability. Challenges: The migration to the cloud presented a series of obstacles for the client, requiring careful attention across multiple areas. Being outdated and tightly integrated with on-premises solutions, their legacy systems, complicated the migration process. Compliance with regulatory standards such as GDPR, PCI-DSS, and SOX was non-negotiable, necessitating an architecture that met the highest security standards. Ensuring uninterrupted financial transactions across regions, efficient disaster recovery, and securing sensitive data through encryption and access controls were top priorities for infrastructure management. Lastly, the client needed a cloud infrastructure that could scale quickly to meet performance demands during peak transaction periods without sacrificing speed or reliability. Our Solutions: We created a secure cloud architecture to solve the client's issues, guaranteeing operational effectiveness, scalability, and security. Phased Migration Approach with Risk Mitigation: We executed a phased migration, starting with non-critical systems to minimize risks. A hybrid cloud strategy ensured smooth integration with on-premises infrastructure. This approach minimized downtime and preserved operational continuity. Effective Multi-Region Disaster Recovery Setup: A multi-region, multi-cloud setup with automated failover mechanisms ensured uninterrupted financial operations and quick disaster recovery. Real-time replication safeguarded critical data across multiple locations. Advanced-Data Encryption and Compliance Measures: End-to-end encryption, strict access controls, and real-time threat monitoring ensured data security and compliance with industry regulations. Ongoing audits strengthened compliance with changing security standards. Serverless Computing for Real-Time Performance: Serverless computing and dynamic scaling enabled real-time transaction processing, fraud detection, and analytics without performance bottlenecks. AI-based strategies improved resource allocation during peak demand. Automated Monitoring for Cost-Efficient Operations: Automated monitoring tools provided real-time insights, while optimized resource allocation reduced infrastructure costs without compromising performance. Predictive analytics helped proactively identify and resolve potential issues. Outcomes: The implemented solution provided seamless migration, enhanced security, and a future-ready infrastructure for sustained growth. Seamless Transition with Legacy Integration: The phased approach ensured a seamless transition without service disruptions, allowing legacy systems to integrate efficiently. Minimal retraining was required as existing workflows remained largely intact. Continuous Financial Transaction Support during Outages: Multi-cloud resilience and failover mechanisms guaranteed continuous financial transactions, even during outages. Regular testing of disaster recovery protocols maintained operational readiness. Protected Financial Data with Automated Compliance Reporting: Advanced security measures protected sensitive financial data while ensuring compliance with industry regulations. Automated compliance reporting simplified audits and reduced administrative burdens. Optimized Performance & Cost Efficiency: Dynamic scaling maintained high-speed processing while reducing infrastructure expenses through efficient resource management. Workload balancing further enhanced system reliability and responsiveness. Future-Ready Infrastructure: The scalable architecture supports business growth, enabling effortless expansion and adoption of new technologies. The cloud-based framework positioned the client for seamless innovation and digital transformation.

Leverage Regami’s Cloud AI/ML services to build intelligent scalable and secure solutions. Home Vision Engineering Cloud AI/ML Intelligent Vision, Powered by the Cloud: A New Era of AI Supercharge your image processing and video analysis with our cloud-based AI/ML. Enhance decision-making, automate processes, and utilize real-time insights—all from the cloud. Contact Us Today Login to Get Brochure Accelerating AI-Driven Image Recognition for Cloud-Based Surveillance View Case Study Security Case study Optimizing Model Training Pipelines for Vision Applications in Retail View Case Study Retail Case study Real-Time Cloud AI Solutions for Autonomous Drone Navigation View Case Study Robotics Case Study At Regami, we specialize in delivering advanced cloud-based AI/ML solutions for vision-based applications. From object recognition and real-time image processing to predictive maintenance and automated analytics, our cloud services enable businesses to extract valuable insights from visual data. With scalable infrastructure, cutting-edge models, and secure deployment, we help unlock the power of AI/ML, transforming the way industries leverage image and video data. Our cloud-powered systems accelerate decision-making, optimize workflows, and reduce operational costs, while ensuring seamless integration and industry-specific customization. Our cloud-based solutions are designed to enhance industries by enabling real-time insights, predictive analysis, and intelligent decision-making through efficient and secure AI-driven processes. Whether it's through our Regami Over-the-Air Update (ROTA), OCR (Percepta), License Plate Recognition (Clarity+), Barcode/QR Recognition (Dexter+), Facial Recognition (Vektor+), Iris Recognition (Optiva+), etc. Regami’s versatile offerings ensure that every visual data interaction is seamless, secure, and optimized for performance. Cloud AI/ML Vision-Based Solutions: Tailored to Your Needs Name* Email* Service Required* Service Required Short answer Schedule a consultation with our experts. Submit Cloud AI/ML Cloud AI Consulting Offering expert guidance on integrating AI/ML solutions into your vision-based systems, ensuring alignment with business objectives and industry standards. Know More Cloud AI/ML Custom AI Software Development Tailored AI solutions designed to meet your unique business needs and unlock the full potential of vision-based systems. Know More Cloud AI/ML Predictive Analytics Leveraging cloud-based AI and ML algorithms to analyze visual data and predict trends, enhancing decision-making and operational efficiency. Know More Cloud AI/ML Intelligent Computer Vision & OCR Implement AI-powered computer vision and Optical Character Recognition (OCR) systems to automate visual data interpretation and enhance accuracy. Know More Cloud AI/ML Smart NLP Engines Develop advanced Natural Language Processing engines for intelligent text understanding and seamless integration with vision-based systems. Know More Cloud AI/ML Dynamic ML Systems Build adaptive machine learning systems capable of continuous learning and evolution to optimize visual data analysis. Know More Cloud AI/ML Vision Solutions for Scalable Innovation Regami combines Cloud AI/ML solutions with device engineering expertise, enabling scalable, efficient, and intelligent hardware tailored for diverse applications. Cloud-Based Smart Surveillance Scalable cloud systems for real-time video analysis, enabling proactive security and insights. AI-Powered Retail Analytics Analyze customer behavior, optimize inventory, promotions, and store layouts from the cloud. Remote Industrial Inspections Automate and scale remote visual inspections, boosting defect detection and operational efficiency. AI-enabled Medical devices Rapidly analyze medical images, improving diagnostics and enabling faster care. Automatic LPR systems Real-time vehicle tracking and traffic enforcement with cloud-based LPR solutions. AI-Powered Dashcam Smart dashcam solutions for incident detection and driver behavior analysis via cloud AI. Bridging Gaps in Cloud AI/ML With Tailored Solutions Data Integration & Security Integrating large volumes of visual data while ensuring secure, private access and compliance with industry standards is crucial for AI/ML model accuracy and security. Scaling AI/ML Models Scaling AI/ML models across cloud platforms requires efficient resource management and optimization. Model Deployment & Management Deploying and managing machine learning models in the cloud while ensuring performance and consistency is a significant challenge. Cost Optimization Managing the costs of cloud-based AI/ML infrastructure without compromising performance is a persistent challenge. Real-Time AI Processing Real-time data processing with AI in the cloud can face latency issues, impacting decision-making in fast-paced environments. Model Drift & Maintenance AI/ML models in the cloud require constant monitoring and updates to address model drift, ensuring accuracy and relevance over time. Securing AI Data in the Cloud Regami collaborated with a financial services firm to integrate sensitive customer data securely in the cloud. We implemented advanced encryption and privacy measures, ensuring regulatory compliance and data security. View Case Study Scaling Predictive Analytics for E-commerce We partnered with an e-commerce company to scale their AI-powered recommendation system. By optimizing cloud resources, we increased recommendation accuracy and scaled the system to handle high traffic. View Case Study Streamlining AI Model Deployment for Healthcare egami helped a healthcare provider streamline the deployment of AI models for diagnostic imaging in the cloud. Our solution reduced deployment time and improved model accuracy. View Case Study Reducing Cloud AI Costs for Retail We worked with a retail chain to optimize their cloud AI/ML infrastructure, cutting operational costs by leveraging efficient resource allocation while maintaining high performance. View Case Study Real-Time AI for Smart Cities Regami assisted a smart city project in deploying real-time AI processing for traffic management, reducing latency and improving system responsiveness across the cloud. View Case Study Managing Model Drift for Predictive Maintenance Regami worked with a manufacturing company to monitor and update AI models for predictive maintenance, preventing model drift and ensuring ongoing accuracy in equipment failure predictions. View Case Study Healthcare Provider Addresses AI Model Transparency Challenges Implementing explainable AI methods helped the healthcare provider gain regulatory approval. This increased trust in their AI diagnostics, ensuring compliance with industry standards and improving model transparency. View Case Study Regami's Software Platforms and SDKs Resources Regami Over-the-Air Meridian ONVIF Percepta OCR Nova Android App Authenta Video KYC Clarity+ LPR Dexter+ Barcode/QR Recognition Vektor+ Facial Recognition Optiva+ Iris Recognition Regami's Hardware Platforms Neurex Smart Camera Platform AI-enabling vision for edge processing and automation.  Innova GigE Camera Low-latency ONVIF GigE cameras with on-board storage & PoE. MerlinPlus USB2.0 UVC Camera USB 2.0 UVC camera with on-board storage & dual streaming. Wave WiFi Camera Low-latency ONVIF WiFi cameras with on-board storage. Armor SerDes Camera HDR cameras with GMSL2/L3 & FPD-Link III/IV technologies.  Falcon USB3.0 UVC Camera High-speed, high-performance USB 3.0 UVC camera. Bolt MIPI Camera AI imaging and vision processing accelerated by GPU.  Merlin USB2.0 UVC Camera USB 2.0 UVC camera for high-performance capture. Standard You Trust, Platforms You Rely On Certifications FCC CE UL ATEX ETL RoHS REACH SoC2 HIPAA Processing Platforms NVIDIA NXP MediaTek Raspberry Pi TI STM Espressif Wireless Platforms uBlox Sierra Nordic Laird Quectel Microchip SimCom Innovation Insights: Explore Our Latest Articles How Cloud AI is Transforming Vision-Based Systems Cloud-based AI is transforming industries by enabling real-time processing of images and videos, enhancing decision-making and automation in various applications. Cloud Impact Optimizing Vision AI Performance in the Cloud Best practices for optimizing the performance of vision-based AI models in the cloud, ensuring efficient operations, scalability, and seamless deployment. AI Performance Cost-Effective Cloud Solutions for Vision-Based AI Strategies for optimizing cloud infrastructure to reduce costs while maintaining high-performance vision-based AI systems and ensuring operational efficiency. Cloud Solutions Related Services Vision Engineering Device Engineering One-stop device engineering services for developing smart, connected IoT solutions, from design and prototyping to manufacturing. Know More Vision Engineering Camera Engineering End-to-end camera engineering solutions for design, calibration, and performance enhancement. Know More Vision Engineering Edge AI/ML Tailored engineering services for implementing AI/ML algorithms on edge devices for low-latency insights. Know More Ready to Bring Your Ideas to Life? 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Camera Engineering Real-Time Vision System for Industrial Drone Camera Client Background: Regami collaborated with a leading industrial drone manufacturer specializing in aerial inspections. These drones, equipped with high-resolution cameras and sensors, are used to capture detailed imagery for industrial applications. However, the client’s existing image processing system faced significant delays in real-time defect detection, slowing down inspection workflows. To address these issues, they partnered with Regami to optimize processing speed, enhance image quality, and improve system performance in demanding environments. Challenges: The client faced significant challenges with their existing system, including high latency in processing high-resolution images, which delayed real-time defect detection. Image quality issues, such as poor performance in low-light and HDR conditions, and lens distortion, further hindered accurate diagnostics. Additionally, managing large data volumes during high-speed flights strained the system, while the non-scalable architecture limited future upgrades. The user interface also lacked real-time feedback, making timely decision-making difficult during inspections. The client sought a solution from Regami to address these issues, enhancing processing speed, image quality, scalability, and real-time feedback for improved inspection accuracy and efficiency. Our Solutions: Regami implemented a series of tailored enhancements to transform the client’s drone vision system: Simplified Real-Time Visual Control: We revamped the user interface for intuitive interaction, enabling operators to zoom into critical areas, identify defects quickly, and streamline decision-making. Advanced Image Data Optimization: Efficient compression algorithms were deployed to minimize bandwidth requirements while maintaining image quality, ensuring smooth real-time image rendering during operations. Dynamic Image Adaptation: Integrated adaptive resolution and compression mechanisms to optimize performance under varying bandwidth and resource conditions, ensuring consistent functionality across diverse environments. Enhanced Sensor Integration: High-resolution sensors were fully optimized to capture detailed imagery without overwhelming the hardware, improving the accuracy and reliability of data collection. High-Speed Data Processing Pipeline: A streamlined data transmission system was developed to reduce latency, enabling real-time actionable insights and rapid decision-making during inspections. HDR and Low Light Enhancements: Image signal processing was fine-tuned for challenging lighting conditions, delivering clear and detailed images in low-light and HDR environments. Outcomes: The implemented solutions delivered significant improvements in the drone vision system's efficiency, accuracy, and scalability: Real-Time Optimization: Faster processing enabled real-time defect detection, reducing downtime and accelerating inspection workflows. Enhanced Processing Speed: Optimized pipelines reduced image processing time by half, ensuring quicker response times during critical inspections. Superior Image Resolution: Improved clarity in low-light and HDR conditions enabled more reliable defect detection, even in challenging environments. Refined Accuracy Metrics: Enhanced image quality and processing speed increased the accuracy of defect detection, ensuring more thorough and dependable inspections. Optimized Drone Performance: Efficient data handling improved the drones' overall performance, allowing stable and effective operations even at high speeds or in complex flight conditions. Scalable System Architecture: The modular design supports seamless integration of future sensor technologies, ensuring long-term adaptability and cost-effective system upgrades.

Cloud Engineering Enhancing Cloud Security for a Healthcare Provider Client Background: The healthcare provider is a leading organization with a network of hospitals and clinics nationwide. They offer a range of services, from diagnostics to inpatient care, and prioritize patient data security and privacy. Recently, the client experienced increasing concerns over cloud security vulnerabilities and the complexity of managing sensitive health information in a remote work environment. Faced with the challenge of securing cloud systems while maintaining compliance with strict healthcare regulations such as HIPAA, they sought assistance from Regami. With our expertise in security systems, Regami was tasked with implementing a secure cloud security strategy that would ensure both compliance and seamless access for medical staff. Challenges: They needed a solution that could safeguard against potential cyber threats while allowing for easy access by healthcare professionals, regardless of location. The complexity of managing large volumes of sensitive information within the constraints of HIPAA regulations made it even more difficult. The client had to ensure that their cloud infrastructure could scale with their growing operations without compromising security. They asked for Regami's help to implement a Security Information and Event Management (SIEM) system that would provide real-time monitoring, threat detection, and compliance management. Our Solutions: Solutions were delivered that addressed the client’s cloud security concerns with a focus on HIPAA compliance and operational efficiency. HIPAA Compliance & Data Security: We ensured full HIPAA compliance by implementing encryption, access controls, and audit logging to protect sensitive healthcare data. User Access Control Management: Granular access control policies restricted patient data access to authorized medical personnel only, reducing risks of unauthorized access. Real-Time Threat Detection & Response: We integrated an SIEM system to monitor and detect threats in real time, enabling immediate responses to security incidents. Scalable Cloud Security: A scalable architecture was designed to support the client's growing operations, ensuring continued security and compliance as they expanded. Seamless Integration for Medical Staff: The security measures were integrated into the healthcare providers’ workflows, allowing secure access to data without disrupting operations. Outcomes: The following outcomes were achieved as a result of Regami’s cloud security solutions: HIPAA Compliance: Full compliance was achieved, ensuring patient confidentiality and avoiding regulatory risks. Enhanced Data Protection: Patient data is now fully encrypted, with real-time threat detection and proactive monitoring in place. Improved Access for Medical Staff: Medical professionals can access patient data securely from anywhere, increasing efficiency without compromising security. Streamlined Cloud Management: Optimized infrastructure and centralized management tools allow for easy scaling and performance monitoring. Cost Efficiency in Security Operations: Automation reduced manual efforts in security tasks, lowering costs and improving response speed.

Start your career with Regami. Learn about available positions, requirements, and how to apply to become part of our team. Careers Innovate, Impact, Inspire: Your Future Starts Here at Regami Apply Now The best way to predict the future is to create it. Abraham Lincoln- Innovation is not only a goal at Regami, but also what motivates us. We have established ourselves as an established company in the service sector because of our unwavering dedication to engineering excellence and passion for product development. We would love to speak with you if you are as excited about developing innovative solutions as we are and want to be a part of a vibrant team that turns concepts into industry-leading goods! We're expanding quickly, and we're always looking for bright, driven people to join our team. At Regami, you can find a place whether you're a motivated newbie or an accomplished professional. Let's work together to create an innovative future. Our Culture: Why Choose Regami We foster a supportive environment where innovation thrives, and personal growth is prioritized. Join us to be part of a culture dedicated to your success. Tech-Focused Environment This ecosystem is built for tech enthusiasts, offering cutting-edge tools, modern infrastructure, and a focus on digital transformation to drive innovation and excellence. Diverse Career Opportunities At Regami, we believe growth is key to success, offering opportunities in engineering, product development, project management, and digital marketing to thrive. Holistic Employee Support We care about our people. Regami provides health benefits, wellness programs, and mentorship initiatives to support both professional growth and personal well-being. Flexible Work Environment We value work-life balance. Regami offers flexible hours, great benefits, and a supportive culture to help you manage personal and professional life easily. Growth & Development Your success is our success. We’re committed to your growth with mentorship programs, and clear career pathways to help you excel and reach your potential. Zero Tolerance for Discomfort We prioritize a safe, comfortable work environment with a zero-tolerance policy against discomfort or harassment, reflecting our commitment to employee well-being. Life at Regami: Where Innovation Meets Opportunity At Regami, we believe that work should inspire, challenge, and reward. Our vibrant culture is designed to support your professional and personal growth while making every day at Regami exciting and fulfilling. Here’s what makes life at Regami exceptional Regami is Hiring! Ready to make an impact? Join our innovative team and shape the future with us! Explore Open Positions Regami is Hiring! Ready to make an impact? Join our innovative team and shape the future with us! Explore open positions

Device Engineering Rugged Mechanical Design for ATEX Zone 0 Sensing Devices Client Background: One of the top producers of specialist vehicles for the mining sector is the customer. They create strong, effective machinery that can function in dangerous conditions. The company needed to develop ATEX Zone 0 certified sensing devices for use in areas where explosive gases and dust are present. Ensuring safety, durability, and reliable performance in such challenging conditions was essential. The client required specialized enclosures and camera systems for detecting harmful gases and pedestrians in real time. Challenges: The main challenge was designing strong and durable sensing devices that met strict ATEX Zone 0 certification standards. The devices had to withstand extreme environmental conditions, including exposure to explosive gases, dust, and mechanical stresses. At the same time, the design had to ensure accurate detection capabilities for gas and pedestrian safety. The client needed to develop enclosures that protected sensitive electronics while maintaining functionality and performance in harsh mining environments. Achieving all these design goals while adhering to safety regulations required careful planning and advanced engineering. Our Solutions: Regami provided a comprehensive mechanical design solution that focused on durability, safety, and reliable performance in hazardous environments, ensuring compliance with stringent industry standards. ATEX Zone 0 Certification Compliance: We designed the enclosures and camera systems to meet ATEX Zone 0 safety standards, ensuring that the devices could safely operate in explosive atmospheres without posing a risk of ignition. This certification guarantees the devices' safe operation in critical environments. Durable Enclosure Design: The enclosures were constructed with high-strength materials that could withstand extreme conditions such as high impacts, high temperatures, and chemical exposure, ensuring long-term durability. This design minimized the risk of component damage and maintained performance in the harshest environments. Advanced Camera System Integration: We integrated rugged camera systems into the design, ensuring reliable performance in low visibility conditions and under harsh environmental factors while maintaining clear detection of gas levels and pedestrian movements. The advanced system allowed for precise monitoring and safety alert systems. Enhanced Environmental Protection: The devices were designed to resist dust, moisture, and extreme temperature variations, guaranteeing reliable performance even in the most challenging mining environments. These protections were vital to ensure the system's integrity in such tough conditions. Efficient Power Management: We optimized power management in the design to ensure the sensors and camera systems would operate efficiently for long periods, reducing the need for frequent maintenance or battery replacements. This optimization helped extend the operational lifespan of the devices. Outcomes: The final design met all safety, performance, and durability requirements, enabling the client to deploy the sensing devices in hazardous environments with confidence, ensuring uninterrupted safety monitoring. Successful ATEX Certification: The devices were successfully certified to ATEX Zone 0 standards, ensuring they could safely operate in hazardous mining environments without risk. This certification allowed the client to confidently deploy the devices in environments where safety is paramount. Enhanced Safety Standards: The rugged devices provided real-time gas and pedestrian detection, enhancing the overall safety of mining vehicles and preventing accidents in hazardous zones. These safety features allowed for quicker response times and reduced operational risks. Improved Durability: The durable enclosures and camera systems withstood the harsh mining conditions, ensuring that the sensing devices performed consistently without damage or downtime. This led to fewer repairs and extended product life. Reliable Performance: The devices provided accurate and continuous monitoring, enabling real-time data collection and enhancing the client's ability to monitor and respond to hazardous situations. The continuous performance ensured that safety measures were always active. Streamlined Operations: T he efficient power management and durable design reduced the need for maintenance, lowering operational costs and ensuring that the systems could operate continuously for long periods without intervention. This optimization enhanced the overall operational efficiency of the equipment.

Experience Transformation Personalized Learning Journey for an EdTech Platform Client Background: In the shifting EdTech environment, platforms face challenges in delivering personalized and effective learning experiences. Traditional platforms often struggle with engagement due to a lack of personalization and adaptability. Our client, a major EdTech platform offering online courses, interactive modules, and real-time assessments, was facing difficulties in customizing learning pathways, managing content distribution, and adapting to students' evolving needs. Regami's approach to experience transformation simplified every step of the customer journey, using creative, customer-centric solutions that increased student engagement and generated business success. Challenges: The platform faced several significant challenges, including an inconsistent user experience across devices and touchpoints due to a lack of seamless integration. Personalized learning pathways were limited, resulting in low engagement and retention rates among students. Content delivery was slow, and the absence of real-time feedback was affecting learning outcomes. As the user base grew, the platform struggled to scale effectively, risking compromised performance and quality during peak usage. Regami stepped in with customized solutions to maximize the user experience, enhance personalization, improve content delivery, and ensure the platform’s scalability. Our Solutions: To address these challenges, Regami implemented its Experience Transformation strategy to create a seamless, personalized, and adaptive learning experience: Dynamic Learning Personalization: Regami integrated machine learning algorithms to dynamically adjust course content and recommend resources based on individual student progress, learning styles, and performance. This approach helped guide students through personalized learning pathways, increasing motivation and engagement. Fast-Track Content Delivery: We deployed an intelligent content delivery system powered by edge computing, ensuring faster access to learning materials with reduced latency. This optimization helped ensure real-time content delivery, enhancing the overall learning experience by eliminating delays and improving engagement. Cross-Platform Consistency: Regami ensured a consistent, seamless experience across web, mobile, and tablet devices, implementing responsive design principles to provide a fluid user interface that adapted to different screen sizes. This integration facilitated easier navigation and uninterrupted access to learning materials, wherever students were. Scalable Cloud Infrastructure: The platform’s infrastructure was migrated to a cloud-based solution, enabling auto-scaling to handle surges in traffic. This flexibility allowed the platform to maintain high performance during peak usage periods, ensuring reliability and reducing the risk of system downtime. Proactive Feedback & Insights: By integrating real-time learning analytics, Regami provided instructors and students with detailed insights into learning progress and areas for improvement. This statistical approach allowed students to take ownership of their learning and enabled instructors to provide timely, actionable feedback. Outcomes: By integrating Regami’s experience transformation solutions, the EdTech platform saw significant improvements across key areas of student engagement, content delivery, and operational efficiency. Improved Engagement: Personalizing learning experiences led to a significant increase in student engagement. Tailored recommendations and adaptive learning paths encouraged students to spend more time on the platform, enhancing learning outcomes. Faster Content Delivery: The new content delivery system drastically reduced load times, enabling students to access learning materials almost instantly, enhancing their overall experience and reducing frustration. Higher Satisfaction and Retention: With a seamless, cross-device experience and personalized learning journeys, students were more satisfied with the platform. This resulted in higher retention rates and more frequent usage, with students returning to the platform for continued learning. Scalable Platform: The cloud infrastructure ensured that the platform could scale as needed without any degradation in performance. As the client expanded their course offerings, the platform adapted to the growing demand, ensuring uninterrupted service. Data-Driven Insights: Real-time analytics provided actionable insights that enabled the platform to continually optimize the learning journey, identify pain points, and make data-driven decisions to further improve engagement and satisfaction.

Camera Engineering Ultra Low Latency Cameras For Autonomous Vehicle Navigation Client Background: Our client is a leading technology company specializing in autonomous vehicle navigation systems. Renowned for developing advanced self-driving vehicle solutions, the company focuses on enhancing safety and performance through cutting-edge sensors and artificial intelligence. Despite significant progress in the autonomous vehicle industry, the client faced a critical issue with camera latency, which hindered the real-time decision-making capabilities of their navigation systems. The latency prevented the vehicles from processing visual data quickly enough to adapt to rapidly changing environments. Recognizing the importance of ultra-low latency for optimal performance, the company approached us to refine their camera systems and meet these stringent requirements. Challenges: The client struggled with latency issues in their camera systems, which impacted the vehicle’s ability to process visual data in real time. As autonomous vehicles rely heavily on cameras for perception, these delays affected key functions such as collision avoidance and navigation accuracy. Slow processing times posed significant safety risks, particularly in fast-moving and unpredictable environments. To ensure timely decision-making, the company needed a solution to reduce latency and enhance the overall performance of their autonomous vehicle systems. Our Solutions: To address the challenges faced by the client, we implemented several advanced solutions: Advanced Image Processing: We deployed specialized algorithms designed to process images quickly, reducing the time taken to analyze visual data. This allowed the vehicle’s system to make faster decisions, improving obstacle detection and overall navigation. High-Performance Camera Integration: Regami Solutions integrated ultra-low latency cameras capable of capturing clearer images at faster frame rates. This enhanced the vehicle’s ability to gather and interpret visual information quickly, supporting rapid decision-making even in challenging conditions. Edge Computing Solutions: To minimize transmission delays, we implemented edge computing, processing data directly on the vehicle instead of sending it to remote servers. This solution significantly improved the vehicle's real-time response time. Optimization for Real-Time Data: We optimized the network architecture, facilitating high-speed data transfer between the cameras and the vehicle’s central processing unit. This reduced bottlenecks, improving communication between sensors and the processing unit. AI-Enhanced Visual Recognition: To further improve performance, we integrated AI-driven image recognition systems that quickly identified and classified objects. This enabled prompt, data-driven decisions, allowing the vehicle to adapt rapidly to environmental changes. Robust Testing and Calibration: We conducted thorough testing and calibration of the camera and sensor systems to ensure they performed consistently under real-world conditions. This evaluation confirmed that our solution could handle environmental challenges while maintaining reliable performance. Outcomes: The outcomes of implementing our solutions were significant: Reduction in Camera Delay: Our ultra-fast processing solutions dramatically reduced the visual data processing time, enabling near-instant decision-making. This enhancement improved both the safety and efficiency of the vehicle. Enhanced Navigation Precision: With faster image processing and advanced recognition, the vehicle's navigation system became more accurate in assessing its surroundings. This led to better obstacle detection and improved route planning in dynamic environments. Improved Real-Time Reaction: The vehicle’s camera system could now quickly adapt to evolving conditions, significantly enhancing its responsiveness. This improvement contributed to safer autonomous driving in urban and rural environments. Reduced Collision Risk: By reducing delay and boosting processing speed, the vehicle could more effectively avoid potential accidents. This improved real-time response allowed for quicker adjustments or stops when necessary, reducing collision risk. Boosted Customer Trust: The upgrades to the navigation system enhanced the client's reputation as a leader in autonomous vehicle safety and performance, building trust among customers and partners. Cost Savings in Development and Deployment: By optimizing the camera systems and leveraging edge computing, the client reduced reliance on cloud computing and external server infrastructure, resulting in cost savings. These resources were then reallocated to further innovation.

Cloud AI/ML Streamlining AI Model Deployment for Healthcare Client Background: The client is a leading healthcare organization with multiple branches, specializing in diagnostic imaging services. To improve patient outcomes and increase diagnosis accuracy, they make use of advanced artificial intelligence (AI). Significant delays prevented the organization from using AI models for its imaging systems, which led to inefficiencies and lost chances for better healthcare services. To increase model accuracy and expedite their AI deployment process, they need an experienced partner. Regami stood out due to its proficiency in implementing AI and providing cloud solutions specifically designed for the healthcare sector. Challenges: The healthcare company was having trouble implementing AI models in its diagnostic imaging systems for many reasons. Their current procedures were unscalable, slow, and prone to errors, which led to prolonged waiting periods for model updates and deployments. This made it more difficult for them to quickly incorporate advanced AI technologies, which had an impact on both diagnostic performance and operational efficiency. To increase accuracy, accelerate deployment, and ensure the models could be quickly updated in a cloud environment, the customer requested assistance. They need a strong solution that would guarantee dependability, cut down on delays, and grow with their demands. Our Solutions: Our approach focused on leveraging advanced cloud infrastructure, automation, and continuous monitoring to streamline the entire deployment process while ensuring high standards of accuracy and security. Cloud-Based Deployment : Regami transferred AI models to the cloud, offering a scalable and flexible platform for faster deployment and real-time updates, ensuring timely diagnostic insights. Automated Model Monitoring : We introduced continuous monitoring to track model performance, ensuring quick identification of issues and minimizing downtime to keep the AI models accurate and reliable. CI/CD Pipeline for Model Updates : A CI/CD pipeline automated model versioning and deployment, reducing update cycles from weeks to hours and enabling faster integration of new AI advancements. Scalable Infrastructure : The cloud infrastructure was designed to scale with growing diagnostic data, ensuring no performance issues and future-proofing the client’s AI deployment. Enhanced Data Security : We integrated encryption and compliance measures to ensure the solution met healthcare regulations and safeguarded patient data against unauthorized access. Collaborative Workflow Integration : Regami worked closely with the client’s team to ensure smooth AI model integration into clinical workflows, promoting higher adoption and improving operational efficiency. Outcomes: The deployment of Regami’s solution resulted in significant improvements across several critical areas of the client’s operations. Here’s how our solution made a difference: Reduced Deployment Time : Cloud infrastructure and CI/CD pipelines enabled faster deployment of AI models, allowing the client to integrate updates in hours rather than weeks, ensuring quick access to the latest AI advancements. Improved Model Accuracy : Continuous monitoring and updates enhanced model consistency and precision, leading to more reliable diagnostic results and better decision-making for patient care. Increased Scalability : The cloud-based solution allowed seamless scaling of AI models to handle growing data volumes without performance issues, ensuring future growth and innovation. Enhanced Operational Efficiency : Automation of tasks like model monitoring and updates reduced manual intervention, minimized errors, and improved overall productivity for both medical and IT teams. Stronger Security and Regulatory Compliance : The solution ensured compliance with healthcare regulations, securing patient data with encrypted storage and transmission and safeguarding against potential breaches. Improved Collaboration Between Teams : A collaborative deployment process fostered better communication among teams, improving integration and ensuring smoother adoption of AI models in clinical workflows.