From Data to Insight: Building an AI-Powered Geospatial Intelligence Platform on Amazon Bedrock
Customer Challenge
Red Hawk Roofing required a modern platform capable of processing large volumes of weather and geospatial data while supporting real-time analytics and future AI use cases.
The existing on-premises environment presented several limitations, including limited observability into system performance and failures, manual infrastructure management leading to operational overhead and lack of automated governance and compliance controls.
Key Challenges
- Limited Infrastructure Scalability
- The platform processed large datasets and computational workloads. The on-premises environment made it difficult to scale infrastructure resources dynamically as data volumes increased.
- High Performance Requirements for Spatial Data
- The platform relies on geospatial queries and spatial analytics across historical weather datasets. The existing database infrastructure struggled to support these workloads efficiently.
- AI and Advanced Analytics Constraints
- Red Hawk Roofing planned to introduce AI-driven analysis capabilities to automate report generation and improve weather forecasting insights. The on-premises infrastructure limited the ability to integrate modern AI services.
- Security and Compliance Improvements
- As the platform expanded, the company needed to improve its security monitoring, logging, and compliance posture, particularly to support SOC 2 readiness.
- Infrastructure Management Overhead
- The existing environment required significant manual infrastructure management, increasing operational overhead and risk of configuration drift.
QyrosCloud Solution
QyrosCloud designed and implemented a cloud-native AWS architecture optimized for data-intensive analytics workloads and scalable API services. The platform was migrated from on-premises infrastructure to AWS using a three-tier VPC architecture and a serverless-first design philosophy to improve scalability, security, and operational efficiency.
To support long-term scalability and reliability, QyrosCloud implemented a comprehensive CloudOps framework focused on observability, governance, automation, and customer enablement.
1Secure Three-Tier AWS Architecture
The solution deployed the application within an Amazon VPC structured into three logical tiers:
• Presentation layer supporting API endpoints and external access
• Application layer hosting business logic and data processing services
• Data layer storing historical weather datasets and analytical outputs
Private subnets were used to isolate backend services, while VPC endpoints were implemented to reduce internet exposure and improve security.
2Serverless Processing and API Architecture
Amazon API Gateway
Provides secure and scalable API endpoints for platform services.
AWS Lambda
Executes application logic, analytical processing, and automated report generation workflows.
Amazon SQS
Supports asynchronous processing pipelines for large analytical workloads.
This architecture allows the platform to process large data workloads without managing traditional server infrastructure.
3Optimized Data Infrastructure for Spatial Workloads
To support large geospatial datasets, QyrosCloud deployed a high-performance data architecture.
Amazon RDS for PostgreSQL with PostGIS
Stores historical weather data and enables high-performance spatial queries.
Amazon S3
Stores large datasets, report artifacts, and archival data.
Amazon ElastiCache
Provides a caching layer that reduces database load and accelerates frequently requested queries.
4Event-Driven Analytics Pipelines
The platform implements an event-driven architecture to process large volumes of weather data efficiently.
Data ingestion and analysis tasks are queued through Amazon SQS, triggering AWS Lambda functions that perform data transformation, spatial analysis, and report generation.
This design enables the system to process analytical workloads concurrently and scale automatically based on demand.
5AI Enablement with Amazon Bedrock
The new architecture prepares the platform for integration with Amazon Bedrock, enabling future AI-driven capabilities such as:
- automated analysis of weather datasets
- intelligent report generation
- natural language summaries of analytical results
- predictive insights based on historical weather patterns
This provides a foundation for incorporating generative AI into the platform without requiring major infrastructure changes.
6Security, Compliance, and Observability
The AWS environment includes centralized security monitoring and compliance evaluation using native AWS services.
Security capabilities include:
- AWS Config with SOC 2 conformance packs
- AWS Security Hub
- Amazon GuardDuty
- AWS CloudTrail
Operational visibility is provided through Amazon CloudWatch, enabling centralized logging, metrics, and alerting.
7Proactive Observability
A centralized monitoring and logging framework was implemented using Amazon CloudWatch.
Capabilities include:
- structured logging across services
- real-time metrics for API performance, pipeline latency, and system health
- custom dashboards for operational visibility
- automated alerts for failures and anomalies
8Automated Governance and Compliance
QyrosCloud implemented automated governance controls using:
- AWS Config with SOC 2-aligned conformance packs
- AWS Security Hub for centralized security posture management
- AWS CloudTrail for audit logging
9Infrastructure as Code (IaC)
All infrastructure was deployed using AWS CloudFormation nested stacks, enabling:
- consistent environments across development and production
- version-controlled infrastructure
- repeatable deployments
Results & Business Impact
Migrating the weather intelligence platform to AWS significantly improved scalability, performance, and operational efficiency. QyrosCloud enabled Red Hawk Roofing to transform its platform into a CloudOps-driven, AI-ready system, delivering measurable improvements in performance, reliability, and operational efficiency while establishing a strong foundation for future innovation.
Parallel data analysis tasks
The new architecture allows Red Hawk Roofing to process significantly larger weather datasets and analytical workloads.
Improved Performance for Spatial Queries
Optimized database infrastructure significantly improves the performance of spatial queries and analytical workloads with Amazon Aurora PostgreSQL with PostGIS.
Reduced Infrastructure Management Overhead
Majority of application workloads running on managed AWS services with infrastructure deployed using AWS CloudFormation for repeatable provisioning.
AI-Ready Platform
Integration readiness for Amazon Bedrock foundation models enables Red Hawk Roofing to introduce advanced analytics and AI capabilities.
MTTD Reduced From 45 minutes to <5 minutes
Mean time to resolution (MTTR) was also reduced from several hours to under 1 hour
Improved Audit Readiness and Security Posture
AWS Config with SOC 2-aligned conformance packs
Reduced Provisioning Time From Days to Hours
All infrastructure was deployed using AWS CloudFormation nested stacks
Customer Enablement
QyrosCloud ensured Red Hawk could independently operate and extend the platform. Deliverables included architecture documentation, operational runbooks and knowledge transfer sessions
About Red Hawk Roofing
Red Hawk Roofing is a Colorado-based residential and commercial roofing contractor specializing in roof replacement, repair, and exterior restoration services. The company serves homeowners and businesses across the Front Range region, providing roofing systems designed to withstand Colorado’s extreme weather conditions, including hailstorms, heavy snow, and high UV exposure.
Visit Red Hawk Roofing →