Architecture | Retina Scan AI

System Architecture - retina-scan-ai

This document is the system-level architecture attachment for the repository. It keeps the technical stack, runtime boundary, data/control flow, deployment surface, and operating assumptions in one place.

Architecture Summary

Repository-local proof surface for applied model pipelines and evidence-backed inference, backed by Python service or lab runtime, Container build surface, Local compose environment.

Runtime And Data Flow

flowchart LR
    User["Clinician or reviewer"] --> Surface["Public demo, CLI, package, or README surface"]
    Surface --> Runtime["Runtime boundary: Python service or lab runtime, Container build surface, Local compose environment, GitHub Actions validation"]
    Runtime --> Control["Control plane: configuration, policies, adapters, and jobs"]
    Control --> Data["Data and artifacts: fixtures, reports, logs, exports, or model outputs"]
    Runtime --> Observability["Observability and validation hooks"]
    Observability --> Handoff["Documented handoff and operating boundary"]
    Data --> Handoff

Primary domain: applied model pipelines and evidence-backed inference.

Stack Surface

Cloud Or Local Deployment Boundary

Operating model: artifact registries, batch and online inference paths, edge/managed serving options, and model monitoring hooks

Deployment patterns

• Containerized runtime path suitable for repeatable local, staging, or managed service deployment
• Model serving envelope with artifact traceability, inference monitoring, and quality drift hooks

Control boundaries

• identity boundary and least-privilege service access
• environment separation for local, staging, and managed runtime paths
• secret storage outside source and deterministic fallback for missing credentials
• observability hooks for logs, metrics, traces, and audit events
• rollback path for deployment, schema, and model changes

Resilience controls

• bounded retries with explicit failure states
• health/readiness checks before operator-facing flows are trusted
• idempotent data or artifact writes where repeat execution is possible
• cost and quota guardrails for hosted services and model adapters

AI And Automation Boundary

Operating model: dataset checks, model evaluation, explainability artifacts, serving boundaries, and human-readable confidence evidence

Engineering patterns

• Track dataset splits, model artifacts, inference outputs, and explainability evidence as first-class assets
• Pair quality metrics with failure modes, human review states, and rollback-ready serving boundaries
• Separate deterministic checks from model-generated output so the system remains testable without external credentials
• Capture prompts, inputs, outputs, and decision metadata as inspectable artifacts instead of hidden side effects
• Gate model-assisted actions with policy, confidence, and fallback states before they reach an operator path

Evaluation and model-risk controls

• deterministic fixtures for CI-safe verification
• golden output or schema checks for generated artifacts
• trace capture for prompts, tool calls, inputs, and outputs
• quality gates that fail closed when evidence is missing

Risks to keep explicit

• dataset leakage
• untracked model artifacts
• silent quality drift
• unsafe decision automation

Attached Architecture References

• Service architecture
• Cloud + AI architecture
• Architecture manifest
• Product operating model
• Quality gate

Local Architecture Guard

python3 scripts/validate_architecture_blueprint.py

CI workflow: .github/workflows/architecture-blueprint.yml.

Update this document whenever runtime entrypoints, data stores, hosted services, model/provider boundaries, or operating assumptions change.