AI-led extraction, confidence scoring, validation rules, human-in-the-loop review, and automated workflow execution
Achieved through AWS migration, cloud-native modernization, intelligent lifecycle management, and optimized cloud storage utilization.
Delivered through Multi-AZ architecture, RDS Multi-AZ deployment, automated snapshots, resilient backup strategy, and near real-time RPO during failover scenarios.
Modernized IDP platform provides the foundation for Agentic AI-led document understanding, classification, extraction, validation, exception handling, and intelligent workflow orchestration
Company Overview
GLIB (Genesis Artificial Intelligence Pvt. Ltd.) is an AI-powered Intelligent Document Processing (IDP) platform that converts unstructured documents into structured, actionable data, helping enterprises automate workflows, improve accuracy, and reduce processing time.
Through AWS migration and cloud-native modernization, GLIB enhanced scalability, resilience, cost efficiency, and built a strong foundation for Agentic AI-driven document operations.
Story Snapshot
With growing document volumes, increasing demand for AI accuracy, and the need to reduce manual processing effort, GLIB partnered with Cygnet to modernize its Intelligent Document Processing platform on AWS. The initiative focused on migrating to a scalable cloud-native architecture, optimizing storage and infrastructure costs, strengthening Multi-AZ availability and disaster recovery, and enabling unified monitoring with real-time observability. This modernization created a secure, resilient, and cost-efficient foundation for GLIB’s AI-led document workflows while preparing the platform for Agentic AI-driven classification, extraction, validation, exception handling, and workflow automation.
At a Glance
GLIB’s modernization journey focused on transforming its existing Intelligent Document Processing platform into a scalable, secure, and cost-optimized AWS-native environment. The initiative replaced current infrastructure with cloud-native AWS services, optimized storage through intelligent lifecycle management, strengthened disaster recovery through Multi-AZ and cross-region resilience, and enabled real-time infrastructure observability.
Beyond infrastructure modernization, the platform was enhanced to support AI-led document classification, extraction, validation, workflow automation, and human-in-the-loop review. This created a future-ready foundation for Agentic AI-driven document operations while improving reliability, reducing cloud and operational costs, and supporting enterprise-grade scale.
Solutions Implemented and Outcomes Achieved
|
Solutions Implemented |
Outcomes Achieved |
|
Migrated current workloads to AWS-native infrastructure using Amazon EC2, Amazon ECS, and Amazon RDS PostgreSQL |
Improved platform scalability, deployment flexibility, and cloud-native readiness for high-volume IDP workloads |
|
Implemented Amazon RDS PostgreSQL with Multi-AZ architecture and automated snapshot-based recovery |
Enabled faster database recovery, improved availability, and stronger disaster recovery preparedness |
|
Integrated AI-led document processing capabilities including OCR, classification, extraction, validation, and confidence scoring |
Improved document processing accuracy, throughput, and automation across enterprise document workflows |
|
Applied Amazon S3 lifecycle policies across raw, processed, and backup document buckets |
Achieved storage cost optimization through automated tiering, archival, retention, and deletion policies |
|
Enabled S3 versioning with cleanup of non-current objects |
Reduced redundant storage consumption while retaining rollback and recovery capability |
|
Designed and validated full-region disaster recovery with cross-region replication and failover readiness |
Achieved near real-time RPO and validated recovery processes during disaster recovery drills |
|
Implemented centralized observability using Amazon CloudWatch, AWS Config, Security Hub, and SNS-based alerting |
Enabled 360-degree operational visibility with real-time monitoring, alerts, dashboards, and escalation workflows |
|
Strengthened security using IAM roles, KMS encryption, private networking, VPC endpoints, and controlled access policies |
Improved security posture, reduced public exposure risks, and supported compliance requirements |
|
Established cost governance using AWS Cost Explorer, Budgets, tagging, and resource-level visibility |
Improved cloud spend transparency, chargeback readiness, forecasting, and ongoing cost optimization |
|
Built a cloud foundation ready for Agentic AI-led document workflows |
Enabled future capabilities around intelligent document understanding, exception handling, recommendations, and autonomous workflow orchestration |
Detailed Solution Narrative
To support growing document volumes, higher AI accuracy expectations, and the need for reliable enterprise-grade processing, GLIB partnered with Cygnet to modernize its Intelligent Document Processing platform on AWS. The engagement began with a detailed discovery and assessment phase, where the existing hosted architecture, document processing workloads, storage usage, integration dependencies, security posture, AI processing needs and operational gaps were evaluated.
Cygnet redesigned and migrated the platform to a scalable AWS-native architecture. Amazon EC2, Amazon ECS, and Amazon RDS PostgreSQL were adopted to support application workloads, containerized services, and resilient database operations. RDS Multi-AZ deployment and automated snapshot-based recovery improved database availability and accelerated recovery during planned and unplanned scenarios.
Storage optimization was also a focus area. GLIB adopted Amazon S3 with bucket-specific lifecycle policies for raw documents, processed outputs, backups, and archived data. Intelligent storage tiering, controlled retention, version cleanup, and automated deletion of non-current objects helped reduce long-term storage costs while maintaining business continuity and recovery readiness.
To improve platform resilience, Cygnet designed and validated a disaster recovery approach covering Multi-AZ availability, backup automation, cross-region replication, database recovery, and service redeployment. This ensured that the platform could continue supporting mission-critical document workflows with minimal data loss and faster recovery during failover events.
Operational visibility was strengthened through a centralized monitoring and alerting framework. Amazon CloudWatch, AWS Config, Security Hub, and Amazon SNS were configured to provide real-time observability across infrastructure, application services, database health, storage usage, and security events. This enabled proactive incident detection, faster response, and improved governance for DevOps and operations teams.
Security and compliance were reinforced using fine-grained IAM roles, KMS-based encryption, private network configurations, VPC endpoints, controlled access policies, and secure data movement patterns. These measures helped reduce public exposure risks and ensured that document data was handled through a secure and governed cloud architecture.
In parallel, cost governance was enabled through AWS Cost Explorer, Budgets, tagging policies, and resource-level usage visibility. This allowed GLIB’s finance and DevOps teams to track infrastructure spend, identify optimization opportunities, support chargeback, and manage cloud resources more effectively.
The modernization also created a strong foundation for GLIB’s next phase of innovation: Agentic AI-driven document operations. With scalable AWS infrastructure, AI-led document processing using AWS AI services such as Amazon Textract & Amazon Bedrock (Nova, Claude, etc.), workflow orchestration, human-in-the-loop review, observability, and secure APIs in place, GLIB is now positioned to evolve from an IDP platform into an intelligent operations platform capable of automating classification, extraction, validation, exception handling, recommendations, and business workflow execution.
Cygnet helped us modernize our Intelligent Document Processing platform on AWS, improving
scalability, resilience, observability, and cost efficiency. This transformation has created
a strong foundation for GLIB’s next phase of Agentic AI-driven document operations.
