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Case Study

Indicator

How Indicator Built an Enterprise-Grade Geospatial Platform Using RevStar’s AWS-Native GPU Strategy

The Client

 Indicator is a cutting-edge geospatial analytics firm specializing in hyperspectral satellite data processing. By analyzing imagery across spectral bands, Indicator provides unprecedented environmental and industrial insights. To meet the demands of enterprise-scale workloads, Indicator engaged RevStar to modernize their platform into a high-performance, cloud-native environment. 

 

 

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Professionals collaborating on cloud software and data workflows at computer workstations.

Summary

TYPE OF PRODUCT

Geospatial Analytics Platform

BUSINESS VERTICAL

Geospatial Technology & Environmental Intelligence

TECH USED

Amazon ECS, Amazon S3, Amazon Aurora PostgreSQL, GitHub Actions, GPU-Enabled AWS Infrastructure

SERVICE
Full-service Development Team—

Solution Architecture, Delivery Management, AI/Data Engineering, DevOps Engineering, Quality Assurance

DESCRIPTION

RevStar modernized Indicator’s geospatial analytics platform through GPU-enabled AWS infrastructure, scalable ingestion pipelines, and cloud-native architecture designed to support hyperspectral processing at enterprise scale.

RESULTS

Enabled faster hyperspectral processing, improved infrastructure scalability, and established a secure AWS-native foundation for future AI-driven geospatial innovation.

RevStar played a key role in strengthening Indicator's infrastructure on AWS, improving both reliability and scalability of our geospatial workflows. What stood out most was how quickly they understood the problem space and translated that into practical, well-executed solutions. The team was highly responsive, collaborative, and great to work with from the very first conversation.
Jesse Uszkay Founder, Indicator

The Challenge

As hyperspectral satellite workloads increased in complexity and scale, Indicator needed a more modern infrastructure capable of supporting high-performance geospatial analytics and real-time data processing workflows.

Existing CPU-based inference systems created latency challenges that limited hyperspectral processing performance, while extremely large imagery datasets introduced operational slowdowns and infrastructure bottlenecks. At the same time, tightly coupled legacy architecture made it increasingly difficult to scale new capabilities and support enterprise-grade reliability, security, and deployment requirements.

To support future AI-driven geospatial innovation, Indicator needed a more scalable, cloud-native foundation optimized for GPU acceleration, large-scale ingestion workflows, and long-term operational growth.

 

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The Problem

Indicator needed a scalable and high-performance way to modernize hyperspectral processing workflows, reduce infrastructure bottlenecks, and support enterprise-scale geospatial analytics.

The Solution

We modernized Indicator’s geospatial analytics platform through a secure AWS-native architecture optimized for GPU-enabled hyperspectral processing and large-scale data ingestion workflows.

The solution introduced scalable containerized infrastructure, cloud-native orchestration, and automated ingestion pipelines designed to improve processing speed, operational reliability, and deployment flexibility across hyperspectral workloads. By leveraging GPU-enabled compute and modular architecture patterns, Indicator gained a more scalable environment capable of supporting enterprise-scale geospatial analytics and future AI-driven expansion initiatives.

Designed with modern DevOps workflows and cloud-native infrastructure best practices, the platform established a stronger operational foundation for long-term scalability, infrastructure resilience, and high-volume dataset processing.

 

What We Built

To support Indicator’s modernization goals, we built a GPU-enabled AWS-native geospatial analytics platform designed to accelerate hyperspectral processing, modernize ingestion workflows, and support scalable enterprise operations.

To support large-scale hyperspectral processing workflows, we deployed GPU-enabled Amazon ECS infrastructure, scalable Amazon S3 ingestion pipelines, Amazon Aurora PostgreSQL, and modern CI/CD automation workflows using GitHub Actions. The solution introduced optimized data handling, containerized processing environments, automated deployment pipelines, and cloud-native infrastructure designed to improve operational scalability, reduce processing latency, and support long-term AI-driven geospatial innovation.

The platform was also designed to support growing dataset volumes and future enterprise expansion by improving infrastructure flexibility, deployment reliability, and large-file processing performance across hyperspectral analytics operations.

The Team

We have a growing cost-effective hybrid team, with onshore Product Managers and developers from our build center in Colombia. Operating three parallel teams for different products and support.
Two people collaborating at a whiteboard covered with sketches and sticky notes, planning a project layout.

Results

RevStar helped Indicator modernize its geospatial analytics infrastructure through scalable GPU-enabled AWS architecture and cloud-native ingestion workflows designed to support enterprise-scale hyperspectral processing. By improving operational scalability, deployment flexibility, and infrastructure performance, Indicator established a stronger foundation for future AI-driven geospatial innovation and large-scale satellite data operations.

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