Storage Cost Reduction with Intelligent Lifecycle Management and Version Cleanup
Faster Database Recovery Through RDS Multi-AZ and Snapshot Automation
Recovery Point Objective (RPO) Achieved During Full Region Failover
Operational Visibility via Unified Monitoring Stack with Real-Time Alerts
Company Overview
GLIB (Genesis Artificial Intelligence Pvt. Ltd.) is a document intelligence platform designed to automate data extraction and classification from enterprise documents using AI-driven pipelines. Initially hosted on Microsoft Azure, GLIB transitioned to AWS to benefit from modern cloud services, superior observability, resilient storage architecture, and lower infrastructure overhead.
Story Snapshot
With increased document volumes and the need for high AI accuracy, GLIB partnered with Cygnet.One to modernize its backend on AWS. The project aimed to build a scalable and cost-efficient infrastructure, enhance disaster recovery, automate lifecycle management, and enable real-time observability. This transformation equipped GLIB with a secure, compliant, and future-ready cloud environment to support its intelligent document processing workflows.
At a Glance
GLIB’s cloud modernization journey focused on replacing legacy Azure infrastructure with AWS-native components. By aligning document workloads with intelligent storage classes, securing data pipelines, and validating full-region failover, GLIB achieved operational resilience and sustained cloud savings—without compromising AI performance or compliance.
Solutions Implemented and Outcomes Achieved
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Solutions Implemented |
Outcomes Achieved |
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Migrated Azure-based workloads to Amazon EC2 and Amazon RDS PostgreSQL |
80% Faster Recovery using RDS Multi-AZ and snapshot restoration during DR drills |
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Integrated AWS Textract for document parsing |
Increased accuracy and throughput in data extraction pipelines |
|
Applied lifecycle policies across raw, processed, and backup buckets in Amazon S3 |
60% Cost Reduction in long-term storage by auto-tiering and deletion |
|
Enabled versioning with cleanup for non-current S3 objects |
Lowered redundant storage while retaining rollback capability |
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Designed full-region failover with cross-region replication and snapshot validation |
<1 Minute RPO, 19-Minute RTO validated during disaster recovery drills |
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Implemented centralized observability using CloudWatch, Config, and SNS |
360° Infrastructure Visibility with automated alarms and dashboards |
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Hardened IAM roles, KMS encryption, VPC endpoints, and access controls |
Improved security posture and ensured regulatory compliance |
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Used Cost Explorer, Budgets, and tagging for cost governance |
Enhanced visibility into cloud spend and resource usage |
Empowering AI-Powered Document Processing with Scalable, Secure AWS Infrastructure
To support its growing document automation workloads and meet increasingly complex data handling requirements, GLIB collaborated with Cygnet.One to modernize its architecture on AWS. The engagement began with a full discovery phase, where Azure components were mapped to AWS-native services for optimal performance and cost-effectiveness.
Amazon EC2 replaced Azure VMs, while Amazon RDS (PostgreSQL) took over database workloads with multi-AZ deployments and snapshot-based recovery for resilience. AWS Textract was integrated into the document parsing pipeline, significantly improving extraction accuracy and performance.
Storage optimization was a key focus area. GLIB adopted Amazon S3 with bucket-specific lifecycle rules—ranging from short-term raw document retention to intelligent archiving of processed data. Lifecycle rules and non-current version cleanup led to a substantial reduction in storage costs while maintaining business continuity.
To ensure uninterrupted service delivery, Cygnet designed and executed a region-level Disaster Recovery (DR) plan with RPO under one minute and RTO under twenty. The drill included ECS service redeployment, RDS replica promotion, and primary region fallback—all of which were successfully validated during March 2025 simulations.
Operational transparency was delivered through an integrated observability framework powered by Amazon CloudWatch, AWS Config, and Security Hub. Alarms and performance dashboards were configured for EC2, RDS, Textract, and S3 usage, while Amazon SNS managed escalation workflows for critical alerts.
Security and governance were reinforced with fine-grained IAM roles, KMS encryption across storage and databases, and private network configurations including VPC endpoints for S3. These measures ensured compliance with internal security standards and eliminated public data exposure risks.
In parallel, AWS Cost Explorer and Budgets enabled the finance and DevOps teams to track and optimize cloud spending. All resources followed a strict tagging policy to facilitate chargeback, forecasting, and cost analysis.
With these systems in place, GLIB now operates on a robust, flexible AWS foundation designed to support future scale, accelerate AI-driven innovation, and deliver enterprise-grade service availability and security.
Tools & Technologies Used
AWS Glue
Managed ETL orchestration
AWS Lambda
Event-driven data triggers
Amazon Redshift
Centralized data warehouse
Power BI
Interactive dashboards and reporting
AWS S3
Storage for raw and processed data
Python & SQL
For data modeling and transformation