AI SaaS Architect — Multi-Tenant AI Platform Design

AI SaaS Platform Blueprint — Agency Meeting Assistant

1. Scope and assumptions

Product: B2B meeting assistant for agencies.

Tenant: agency.

Sub-scope: client workspace.

Users: agency members and invited client reviewers. Workload, cloud, storage, model provider, jurisdictions, and target plan limits are unspecified. This is a design proposal only; no implementation or compliance review has occurred.

2. Domain and tenant boundaries

Represent AgencyTenant as the authoritative customer boundary and ClientWorkspace as a child scope. Users join a tenant through explicit membership; workspace access derives from membership plus workspace role. Every meeting, note, summary, action item, approval, file, job, audit event, and AI context record carries a server-resolved tenant and workspace scope. Do not trust tenant IDs supplied by a browser.

Enforce scope in the API authorization layer and again in data access policies. Include tenant scope in object storage paths, vector metadata and filters, cache keys, queue payloads, search indexes, exports, and observability fields. Design cross-tenant tests with synthetic markers before launch.

3. Roles and permissions

Separate authentication, membership, role, resource ownership, and plan entitlement. Enforce permissions server-side. Require explicit sharing before client access.

4. Plans, usage, and billing

Store plan definitions and limits as versioned data. Meter at the authoritative processing boundary, for example accepted audio minutes, generated outputs, and active workspaces, after confirming the commercial unit with the buyer. Record tenant, meter, quantity, time window, idempotency key, and plan version. Keep immutable usage events separate from invoices and provider cost records. The billing ledger—not model output or cache state—is the financial source of truth. Handle retries idempotently and define over-limit behavior before launch.

5. AI processing workflow

1. Authorize the user and resolve tenant/workspace scope.
2. Validate upload type, size, and user permission; store encrypted source data under scoped paths.
3. Create a job with an idempotency key, tenant reference, status, and retention deadline.
4. Send only the minimum necessary content to the selected model adapter under documented provider terms.
5. Store outputs with source provenance and the same tenant boundary; require human review before client sharing.
6. Emit metering and audit events; expire temporary artifacts according to an explicit retention policy.

6. Reliability and observability

Use a durable job queue, bounded retries with backoff, dead-letter handling, cancellation, and safe resume. Make output creation idempotent to avoid duplicate summaries or charges. Monitor job success, latency, queue age, provider errors, cost per tenant, authorization denials, and quality evaluation outcomes without logging raw meeting content by default. Define operator escalation, customer status, and recovery targets after workload expectations are known.

7. Launch gates and open decisions

Before production, confirm cloud and data regions, provider retention/training terms, recording consent and privacy requirements, actual workload and latency targets, deletion/export needs, plan thresholds, support access procedure, and incident ownership. Verify tenant isolation, role boundaries, metering correctness, retry safety, retention behavior, and quality on representative authorized test data. Obtain qualified security and privacy review. No compliance status or test result is asserted here.