Back office ops · Production
Building LLM Platforms for Enterprise on AWS: Architecture for Compliance-Heavy Deployments
The problem
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.
Workflow diagram · grounded in source
1
Data ingestion and vectorization
integration
“facilitates parsing, chunking, metadata enrichment, and vectorisation, leading to an organised vector database”
2
Semantic query caching
ai_action
“semantically similar queries are not quantitatively identical, for instance: "How much are the adidas trainers" and "How expensive are those Adidas" semantically require the same response. There is no point making a round trip for the la…”
3
LLM Gateway routing
routing
“Embracing 'LLM gateway' reflects a readiness to separate LLM APIs from applications, enabling swift replacement of LLMs—a crucial factor given the rapid evolution and diversity of models”
4
RAG knowledge retrieval
ai_action
“It provides data and retrieval-augmented generation (RAG) capabilities to the Application Layer”
5
LLM evaluation pipeline
validation
“The AI Factory is designed to perpetuate a cycle of continuous improvement through an LLM Evaluation Pipeline. This could include A/B testing frameworks leveraging the model variants feature in SageMaker, allowing for comparative analysi…”
6
Chatbot user interface
output
“I also always like to include the possibility of a chatbot seeing as chatbots represent the most natural way to converse”
7
Cost and usage reporting
feedback_loop
“The Reporting Layer, integral for transparency in costs, usage, and data analytics, is implemented using AWS services like CloudWatch and Cost Explorer”
Reported outcome
(not stated)
Reported stack
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
Source
https://mlops.community/blog/building-llm-platforms-for-your-organisation-step-2-platforming
Read source ↗Frequently asked questions
What did this team achieve with this AI workflow?
(not stated)
What tools did this team use?
Amazon S3, Step Functions, AWS Managed Airflow, LangChain, LlamaIndex, OpenSearch, Qdrant, Chroma, AWS Neptune, Amazon DynamoDB.
How is this back office ops AI workflow structured?
Data ingestion and vectorization → Semantic query caching → LLM Gateway routing → RAG knowledge retrieval → LLM evaluation pipeline → Chatbot user interface → Cost and usage reporting.