01 · Client overview
Client overview
ONZE Technologies (INDIA) Pvt Ltd (Latlong.ai) operates in the geospatial and location intelligence domain, providing APIs and analytics for capabilities such as mapping, geocoding, routing, and geofencing. The organization focuses on building developer-friendly location solutions that enable seamless integration of location intelligence into applications and business workflows. The platform supports use cases across industries, including logistics, transportation, financial services, healthcare, and other data-driven sectors, where accurate, scalable, and real-time location insights are critical for operational efficiency and informed decision-making.
02 · Challenge
Challenge
ONZE Technologies (Latlong.ai) identified key opportunities to enhance and scale its location intelligence platform more effectively. The existing infrastructure presents scope to improve elasticity, enabling seamless scaling of compute and database resources without service disruption, while evolving provisioning processes and microservices management can further accelerate expansion across environments. Operational maturity can be strengthened by adopting a robust orchestration framework and standardized deployment processes, ensuring consistent environments, reducing release dependencies, and improving documentation for better troubleshooting and onboarding. Cost optimization opportunities exist through better alignment of resource utilization, where implementing auto-scaling can help optimize spend during low usage periods while maintaining performance during peak demand. Additionally, enhancing centralized identity management, security controls, and audit logging can enable stronger governance, consistent policy enforcement, and improved visibility into system and user activities. Performance optimization remains a key focus area, where integrating efficient content delivery mechanisms can reduce latency for global users, especially for static and map-based content, enabling secure, reliable, and scalable regional expansion.
03 · Solution
Solution
To address operational and scalability challenges, Comprinno modernized the infrastructure on AWS in the Mumbai (ap-south-1) region, establishing a secure, scalable, and future-ready cloud foundation. The solution focused on migrating existing workloads with minimal disruption while stabilizing environments, improving performance, and strengthening security and governance. AWS-native services were used to enable elasticity, high availability, global content delivery, and cost optimization.
Key Actions
Migration & Modernization :
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Migrated existing workloads to AWS with minimal downtime and zero data loss.
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Stabilized and optimized both staging and production environments to ensure reliable operations.
Scalable Infrastructure Foundation :
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A secure, multi-layer AWS architecture was designed and implemented to provide high availability and fault tolerance.
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This foundation enabled the environment to scale seamlessly and handle rapid growth and traffic spikes without service disruption.
Application & Service Management :
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Applications were moved to AWS ECS to enable centralized and fully managed container operations.
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Auto-scaling mechanisms ensured consistent application performance during fluctuating demand and peak usage periods.
Traffic & Performance Optimization :
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Application Load Balancer was used to efficiently distribute traffic and improve response times.
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AWS CloudFront enabled global, low-latency delivery of static and map-based content, enhancing performance for geographically distributed users.
Data & Storage Optimization :
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AWS Aurora Serverless v2 was implemented to provide scalable and reliable data management aligned with workload demand.
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Storage was secured and centrally managed, supported by automated backups and lifecycle policies to improve durability and cost efficiency.
Security, Monitoring & Cost Control :
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Strong access controls and encryption were enforced using AWS IAM
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Amazon CloudWatch enabled real-time monitoring, logging, and proactive alerting, while automation and right-sizing practices helped control costs and prevent overspending.
The solution is deployed on AWS using a secure, multi–Availability Zone architecture designed to support large-scale, location intelligence workloads. The environment operates within isolated Virtual Private Clouds for staging and production and follows a layered network design to ensure high availability, scalability, and controlled access. User traffic is routed through an Application Load Balancer to containerized microservices running on Amazon ECS with AWS Fargate across multiple Availability Zones. Outbound connectivity is securely managed, while services scale automatically based on demand.
The data layer is powered by Amazon Aurora Serverless v2, deployed in private subnets to deliver high availability, low latency, and zero-downtime scaling. Centralized security, monitoring, and governance are enforced using AWS IAM, AWS KMS, AWS Secrets Manager, Amazon CloudWatch, and AWS security services. The architecture provides a resilient, cost-efficient, and scalable foundation capable of handling traffic spikes, global access, and future platform growth.
04 · Results
Results
Benefits
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Enhanced Security & Compliance – The modernized cloud environment follows a strong defense-in-depth security model with centralized access controls, encryption, continuous monitoring, and automated threat detection. This significantly reduced security risks, improved visibility, and ensured that sensitive data is protected in line with industry best practices and compliance requirements.
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Improved Scalability & Platform Resilience – The architecture is designed to handle rapid growth, unpredictable traffic patterns, and increasing data volumes with ease. The platform can now scale seamlessly to support new customers, regions, and use cases without infrastructure bottlenecks or performance degradation.
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Higher Operational Efficiency & Reliability – Adoption of managed and automated cloud services minimized manual intervention and simplified operations. Deployments became faster and more reliable, while centralized monitoring enabled quicker issue detection and resolution
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Optimized Costs & Sustainable Growth –Infrastructure costs are closely aligned with actual usage through automated scaling and right-sizing. Over-provisioning was eliminated, reducing waste and ensuring a pay-as-you-use model that supports long-term financial efficiency and sustainable growth.
Conclusion
Comprinno helped convert a complex, resource-intensive environment into a scalable, secure platform on AWS. The foundation empowers the organization to confidently expand its geospatial and location-intelligence offerings, onboard new clients, and introduce advanced capabilities without being constrained by infrastructure limitations. With strong automation, built-in resilience, improved performance, and enterprise-grade security, the geospatial intelligence platform is now positioned for accelerated growth and innovation. This demonstrates how a well-executed cloud modernization strategy can turn scalability challenges and operational complexity into a robust competitive advantage in the rapidly evolving geospatial technology landscape.