applied-ai-architect-commercial-enterprise

Applied AI Architect — Commercial & Enterprise

When to Use

• Design end-to-end AI architecture for a B2B product feature or internal copilot
• Choose between RAG, fine-tuning, agents, or workflow orchestration for a use case
• Define multi-tenant isolation, data boundaries, and customer data handling for AI
• Map enterprise requirements: SSO, audit logs, residency, DLP, human oversight
• Compare cloud AI platforms (managed endpoints, private networking, quotas)
• Author AI-specific ADRs for engineering, security, and procurement
• Plan POC → pilot → production with eval, safety, and ops gates

When NOT to Use

• Write application code, prompts, or eval harnesses → ai-engineer, prompt-engineer
• General non-AI integration ADRs → senior-system-architecture
• Context packing and token budgets → ai-context-engineer
• Memory store implementation → ai-memory-developer
• AI risk tiering and policy documents → ai-risk-governance
• SOC 2 evidence packs → compliance-engineer
• Token cost improvement program → ai-token-improvement-plan-engineer
• AI ops cadence and release governance → ai-lead-ops
• Business case and operating model → business-consultant

Related skills

Need	Skill
Build and ship RAG/agents	ai-engineer
Cross-service platform ADRs	senior-system-architecture
Governance and use-case classification	ai-risk-governance
Security architecture and controls	cybersecurity, information-security-engineer
Data platform for embeddings/warehouse	data-architect
Product tenant isolation	product-infrastructure-security-engineer
Red-team before launch	ai-redteam
Commercial contract terms	commercial-counsel

Core Workflows

1. Classify context: commercial vs enterprise

Lens	Commercial (product)	Enterprise (internal IT)
Users	Customer tenants	Employees, partners
Data	Customer content + config	Corp IP, HR, regulated data
Identity	Product auth + tenant ID	IdP (Entra/Okta), groups
Isolation	Strong multi-tenancy	Network/VPC, private endpoints
Buying	SKU, usage metering	EA, private offer, on-prem option

See references/commercial_vs_enterprise.md.

2. Shape the solution

1. Job — user outcome in one sentence; success metric
2. Pattern — Q&A RAG, task agent, workflow automation, codegen assist
3. Data — sources, freshness, PII, retention, who may see what
4. Model — capability vs cost; routing; fallback
5. Control plane — prompts, tools, policies, human approval points
6. Plane separation — ingestion ≠ inference ≠ logging (scale independently)

See references/reference_architectures.md.

3. Non-functional requirements

Document targets for:

• Latency (p95 first token, end-to-end task)
• Availability and DR for inference path
• Cost per tenant/session and guardrails
• Auditability (prompt, retrieval IDs, model version—no secrets in logs)
• Safety (tier, blocked topics, escalation)
• Compliance (residency, retention, subprocessors)

See references/enterprise_nfr_integration.md.

4. Platform and vendor selection

Score options on: data handling, private link, model catalog, SLAs, metering, exit strategy.

See references/platform_selection.md.

5. AI ADR and review

For one-way doors (vendor, data leaves region, autonomous agents):

• Options, NFR impact, security, cost model
• Migration and rollback

See references/ai_architecture_decision.md.

Pair with senior-system-architecture for org-wide integration standards.

6. POC → production path

Stage	Architecture focus
POC	Single tenant, manual eval, no prod data
Pilot	Tenant isolation, observability, limited users
GA	SLOs, rate limits, on-call, eval in CI, red-team for tier-2+

See references/poc_to_production.md.

When to load references

• Commercial vs enterprise → references/commercial_vs_enterprise.md
• RAG, agent, copilot patterns → references/reference_architectures.md
• SSO, DLP, logging, residency → references/enterprise_nfr_integration.md
• Cloud AI platform choice → references/platform_selection.md
• AI ADR template → references/ai_architecture_decision.md
• Production hardening → references/poc_to_production.md