Intelligent Document Processing (IDP) 7x Faster with Claude on Amazon Bedrock and Agentic AI-Driven Operations 

With growing document volumes, rising demand for AI accuracy, and the need to reduce manual processing effort, GLIB partnered with Cygnet to modernize its IDP platform on AWS and put a production-grade AI model at the center of its document workflows. The initiative had to migrate to a scalable cloud-native architecture, optimize storage and infrastructure costs, strengthen Multi-AZ availability and disaster recovery, enable unified real-time observability, and critically move AI-led classification, extraction, and validation from rules-based and ML model-based processing into a reliable, governed, model-driven pipeline.

GLIB’s IDP platform runs Amazon Bedrock – Claude in production for document classification (along with rule-based approach), extraction, and validation, wrapped in confidence scoring and human-in-the-loop review, on a scalable, secure, cost-optimized AWS-native foundation. The engagement migrated the platform to cloud-native AWS services, optimized storage through intelligent lifecycle management, strengthened disaster recovery through Multi-AZ and enabled real-time observability while operationalizing the Claude-driven AI pipeline that processes live document workloads today.

At the core of IDP document intelligence is Claude, accessed through Amazon Bedrock, running as the model that classifies incoming documents, extracts structured fields, and validates them against business rules. Claude, accessed through Amazon Bedrock, is the deployed engine behind its live document pipeline.

Model: Claude Sonnet 4.5 on Amazon Bedrock is the inference model behind live agent decisions in production.

What Claude on Bedrock does today

• Document classification (along with rule-based approach) — routes each incoming document to the correct type and workflow.
• Field-level extraction — converts unstructured content into structured, validated data.
• Validation & exception handling — applies business rules, flags low-confidence results, and routes exceptions to human-in-the-loop review.
• Agentic orchestration — IDP agentic layer, built on the Strands Agents SDK and an in-house orchestration framework, sequences and coordinates multi-step document workflows around Claude on Bedrock.

Responsible AI

Claude’s inputs and outputs are governed by Amazon Bedrock Guardrails content-safety filtering, prompt-injection defense, and a use-case-specific denied-topic policy configured and active on the production account. This runs alongside, and is distinct from, the application-level confidence scoring that routes low-confidence extractions to human review. Together they give GLIB governed, auditable automation: Guardrails constrain what the model is allowed to say and act on; confidence thresholds decide when a human must look at what it produced.

To support growing document volumes, higher AI-accuracy expectations, and reliable enterprise-grade processing, GLIB partnered with Cygnet to modernize its IDP platform on AWS and operationalize Claude on Amazon Bedrock as its document-intelligence engine. The engagement began with a discovery and assessment phase evaluating the existing architecture, document workloads, storage usage, integration dependencies, security posture, AI processing needs, and operational gaps.

Cygnet redesigned and migrated the platform to a scalable AWS-native architecture. Amazon EC2, Amazon ECS, and Amazon RDS PostgreSQL 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.

On this foundation, Cygnet transformed and operationalized the AI pipeline: Claude on Amazon Bedrock performs classification, extraction, and validation, with Amazon Textract feeding OCR where required. GLIB’s agentic layer, built on the Strands Agents SDK and an in-house orchestration framework, sequences and coordinates these steps around Claude. Outputs pass through Bedrock Guardrails and confidence-based human-in-the-loop review before downstream workflow execution: a governed, production-grade model-driven pipeline rather than a rules-only process.

IDP adopted Amazon S3 with bucket-specific lifecycle policies for raw documents, processed outputs, backups, and archived data. Intelligent tiering, controlled retention, version cleanup, and automated deletion of non-current objects reduced long-term storage costs while maintaining continuity and recovery readiness.

Operational visibility was strengthened through a centralized monitoring and alerting framework. Amazon CloudWatch, AWS Config, Security Hub, and Amazon SNS provide real-time observability across infrastructure, application services, database health, storage usage, and security events — extended with GenAI observability over the Claude/Bedrock pipeline, including model invocation, guardrail interventions, and agent workflow health, for proactive detection and governance.

Security and compliance were reinforced with fine-grained IAM roles, KMS-based encryption, private network configurations, VPC endpoints, controlled access policies, and secure data-movement patterns — reducing public-exposure risk and ensuring document data is handled through a secure, governed cloud architecture. Claude’s inputs and outputs run behind Amazon Bedrock Guardrails, configured for content safety, prompt-injection defense, and a use-case-specific denied-topic policy.

Cost governance was enabled through AWS Cost Explorer, Budgets, tagging policies, and resource-level usage visibility, allowing GLIB’s finance and DevOps teams to track spend, identify optimization opportunities, support chargeback, and manage resources effectively.