01 · Context
Operating environment
AI-enabled SaaS products add model calls, retrieval stores, evaluation data, and usage economics to the familiar concerns of identity, tenancy, authorization, and product operations.
02 · Business problem
The condition that required change
The product must preserve tenant boundaries while supporting fast iteration, trustworthy evaluation, and understandable operating cost.
- Tenant data and prompt isolation
- Variable model cost and latency
- Evaluation across product releases
- Provider portability and failure modes
03 · Architecture decisions
Why the system takes this shape
Carry tenant context through every boundary
Authorization, retrieval, model requests, logs, and cost attribution share the same explicit tenant scope.
Put an application boundary around models
A model gateway centralizes policy, provider choice, fallback, budgets, redaction, and telemetry.
Treat evaluation as product infrastructure
Offline test sets and production feedback connect model changes to user and business outcomes.
04 · System design
Architecture and delivery
The reference design connects identity and tenant authorization to domain services, isolated retrieval, a governed model gateway, evaluation pipelines, observability, and per-tenant usage controls.
This is a reference pattern rather than a deployed system. Concrete isolation, compliance, availability, and cost decisions depend on product risk and customer commitments.
05 · Outcomes
Measured or intended results
Tenant-safe AI capabilities with traceable usage and decisions.
Model and provider changes evaluated as product releases.
06 · Lessons
What transfers to other systems
- Tenant identity must survive every AI and data boundary.
- Evaluation, observability, and cost attribution belong in the platform design.
- Model abstraction is useful only when it preserves provider-specific evidence and behavior.