A DevOps team uses Terraform to provision a complete MLPS 2.0-hardened production stack (VPC, ECS cluster, OSS, RDS, SLB with HTTPS via CAS certificates, Elasticsearch for RAG), then layers Bailian OCR document intelligence on top so that scanned enterprise documents uploaded to OSS are automatically recognized, structured, and indexed into Elasticsearch for hybrid full-text and semantic retrieval—all behind a compliant, certificate-secured application tier.
A DevOps team uses Terraform to provision a complete MLPS 2.0-hardened production stack (VPC, ECS cluster, OSS, RDS, SLB with HTTPS via CAS certificates, Elasticsearch for RAG), then layers Bailian OCR document intelligence on top so that scanned enterprise documents uploaded to OSS are automatically recognized, structured, and indexed into Elasticsearch for hybrid full-text and semantic retrieval—all behind a compliant, certificate-secured application tier.
See _combos/secure-app-with-structured-and-semantic-search-469afa.
See _combos/secure-ocr-enhanced-enterprise-app-with-hybrid-s-d184de.
See _combos/full-production-stack-with-ssl-and-rag-search-50664b.
See _combos/terraform-enterprise-app-with-secure-search-e08b2e.
Q: How do I deploy an MLPS-compliant production stack with Terraform that integrates Bailian OCR for scanned documents and enables semantic search? A: You provision this environment by using Terraform to deploy a complete MLPS 2.0-hardened production stack and then layering Bailian OCR document intelligence on top. The setup includes VPC, ECS cluster, OSS, RDS, and SLB with HTTPS via CAS certificates alongside Elasticsearch for RAG. Scanned enterprise documents uploaded to OSS are automatically recognized, structured, and indexed into Elasticsearch for hybrid full-text and semantic retrieval behind a compliant, certificate-secured application tier.