GenAI-Powered Document Processing on AWS
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How Datup migrated its AI platform from OpenAI to AWS Bedrock and scaled SuplAI with a secure RAG architecture
AI-driven supply chains demand more than models — they require scalable infrastructure, predictable costs, and secure data handling.
Datup partnered with binbash to migrate its SuplAI platform to AWS, implementing a Retrieval-Augmented Generation architecture powered by Amazon Bedrock. The result was a more scalable, cost-efficient, and production-ready GenAI platform designed for real business growth.
Rapid AI growth created cost and scalability pressure
Datup’s SuplAI solution experienced a 200% increase in usage, making the previous AI stack unsustainable from both a cost and scalability perspective.
Key challenges included:
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Rising operational costs driven by external AI APIs
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Need for scalable infrastructure to support growth
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Security and privacy requirements for sensitive customer data
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Improving response speed and reducing manual workflows
The goal wasn’t simply migrating models — it was rebuilding the foundation for long-term AI scalability.
A Well-Architected GenAI platform built with AWS Bedrock and binbash Leverage™
binbash implemented a full AWS-native architecture designed to optimize performance, governance, and developer efficiency.
Core components included:
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Multi-account AWS Landing Zone for governance and cost control
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Kubernetes EKS clusters with managed nodes and spot instances
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Retrieval-Augmented Generation architecture powered by Amazon Bedrock
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OpenSearch vector database for semantic retrieval
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Streamlit-based frontend for seamless user interaction
This approach allowed Datup to maintain advanced AI capabilities while improving cost efficiency and infrastructure control.
From external AI dependency to scalable GenAI infrastructure
The new architecture introduced:
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Foundation models and embeddings through Bedrock
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Vector search powered by OpenSearch
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Secure orchestration across Kubernetes workloads
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Automated pipelines for deploying containerized AI services
The RAG workflow connects Bedrock, vector search, and application services, enabling faster responses and more accurate insights.
Security and Governance by Design
Handling supply chain data required a strong security baseline.
The implementation included:
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CloudTrail, GuardDuty, and IAM Access Analyzer for monitoring
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Encryption with AWS KMS
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Secure multi-account architecture
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Restricted S3 access policies to protect sensitive data
Security was embedded into the architecture — not added later.
Automation and DevOps Acceleration
To reduce operational friction, binbash introduced:
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CI/CD pipelines for container deployments on EKS
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Infrastructure-as-Code with Terraform and ArgoCD
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Automated provisioning workflows aligned with binbash Leverage™
This enabled Datup’s teams to ship AI improvements faster without increasing infrastructure complexity.
Better performance, lower costs, and scalable AI growth
Key results achieved:
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35% reduction in operational costs after migrating to Bedrock
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Infrastructure ready to support 200% growth in usage
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Increased response accuracy to 90%
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Faster response times reduced to 30 seconds
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Improved data accessibility and decision-making for customers
Datup transformed its AI platform from a scaling challenge into a strategic advantage.
Ready to scale your AI workloads securely?
