<- Back to featured projects

Draft Case Study

Azure AI / RAG Infrastructure

Organizations exploring RAG require secure and governable infrastructure foundations before experimentation at scale.

Azure AIRAGInfrastructureSecurity

Problem

Organizations exploring RAG require secure and governable infrastructure foundations before experimentation at scale.

Architecture

Draft architecture outlines ingestion, vector indexing, model access, and private networking controls.

IngestionIndexRetrievalModel
Sanitized RAG infrastructure diagram showing ingestion, indexing, and retrieval flow.

Reference/proposed architecture diagram for portfolio demonstration. Not a client production architecture.

Architecture Explorer

RAG Infrastructure: Governed ingestion, indexing, retrieval, and model response path.

Proposed

Data flow: Curated content ingests into indexing, retrieval enriches prompts, and response generation occurs through controlled model access.

Accessible alternative: use the component buttons below to inspect architecture details without zoom.

Legend

  • Ingress / Trigger
  • Orchestration
  • Data / Storage
  • Security
  • Observability
  • Operations
  • Document Ingestion -> Vector Index (content flow)
  • Vector Index -> Retrieval Layer (retrieval)
  • Retrieval Layer -> Model Endpoint (prompt context)
  • Retrieval Layer -> Guardrails (policy check)
  • Guardrails -> Model Endpoint (approved prompt)
  • Model Endpoint ~> Response Logging (response telemetry)

Component Details

Document Ingestion

Collect and normalize content for retrieval use.

Responsibilities: Parsing | Chunk preparation

Security controls: Input sanitization | Content access filtering

Dependencies: Vector Index

Design decisions: Keep ingestion isolated from model runtime surface.

Security

Draft security includes identity boundaries, data handling controls, and auditability patterns.

Verified Outcomes

  • Draft: safer baseline for RAG experimentation under governance constraints.
  • Draft: clearer separation of concerns across ingestion, indexing, and retrieval layers.

Design Decisions

  • Isolate ingestion, retrieval, and model-access boundaries to control data exposure.
  • Prefer private networking and identity-scoped access for AI runtime services.
  • Standardize observability signals early to support safe experimentation at scale.

Implementation

  • Provision ingestion and indexing infrastructure with environment-level isolation.
  • Configure retrieval and model-call pathways with controlled identity boundaries.
  • Add monitoring and policy checks for model usage and data handling flows.

Trade-offs

  • Stronger security controls can add complexity to initial prototyping speed.
  • Isolated environments improve governance but increase operational overhead.

Engineering Evidence

Reference architecture evidence

Architecture Evidence

Diagram classification: Proposed

Sanitized data flow: Document ingestion feeds vector indexing, retrieval assembles context, guardrails enforce policy checks, and model responses emit sanitized telemetry.

Diagram source id: azure-ai-rag-infrastructure

Evidence pending: no implemented demo route is linked for this case study.

Automated Tests and Results

  • Diagram validation and explorer rendering

    tests/architecture-explorer.test.ts

Latest test run (2026-09-26): Evidence pending: add executable modules and targeted tests for this case study.

Command: No case-study-specific automated tests are currently implemented for this architecture draft.

Latest build run (2026-09-26): Portfolio build passes, but this case study still lacks project-specific implementation artifacts.

Command: node_modules/.bin/next.cmd build

Limitations and Verification Gaps

  • No live RAG service code, index schema, or model-call modules are committed.
  • No retrieval quality, guardrail, or latency benchmark test evidence is available.

Evidence Pending

  • Add executable ingestion, indexing, and retrieval modules with test dataset fixtures.
  • Add automated evaluation and guardrail tests with reproducible outputs.

Evidence Status

  • Local checks: Architecture/content checks only.
  • CI checks: Repository lint/type/build checks available.
  • Azure deployment: Not performed / Evidence pending.

Reproduction and Dependencies

Dependencies

  • Node.js (current project uses Next.js 16.x, React 19.x, TypeScript 6.x)
  • npm install
  • Local binaries under node_modules/.bin for lint/typecheck/test/build

Reproduction steps

  1. npm install
  2. Review architecture model in lib/architecture-explorer-data.ts
  3. Open the project case-study route and inspect Architecture Explorer
  4. Implementation evidence requires source modules and automated tests specific to this case study