back_office_ops · workflow
Building LLM Platforms for Enterprise on AWS: Architecture for Compliance-Heavy Deployments
Compliance-heavy enterprises in pharma, finance, and healthcare face significant constraints when deploying LLM-based Knowledge Assistants at scale, including GDPR and HIPAA adherence, centralised RBAC API governance, multi-account AWS separation of duties, and the need for LLM agnosticism as models rapidly evolve.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Data ingestion and vectorization
Enterprise data is parsed, chunked, enriched with metadata, and vectorized into an organized vector database.
Tools used
Amazon S3Step FunctionsAWS Managed AirflowLangChainLlamaIndexOpenSearchQdrantChromaAWS NeptuneAmazon DynamoDBAmazon SageMakerSageMaker StudioSageMaker JumpStartHuggingFaceAnthropicCohereAmazon CloudWatchAmazon API GatewayAWS CodePipelineAWS CodeCommitAWS CodeBuildAWS CodeDeployECRCost ExplorerAWS OrganizationsCloudFormationAWS ConfigAWS Control TowerAWS Service CatalogAmazon EventBridgeEKSECSFargateLambdaReactDockerKedroGitHubGitHub ActionsTerraform
Outcome
(not stated)
Grounding & classification
Source type: technical build writeup
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chatbotenterprise searchragknowledge basetools describedworkflow describedfinancial serviceshealthcarepharma life sciencestechnical build writeupback office opsrag answering