Production-AI Readiness Checklist

Vector embeddings, metadata, and document caches are partitioned with row-level security (RLS) and cryptographic tenant keys.

Explicit contractual and API-level assurance from all model vendors that enterprise prompt data is never retained for training.

Automated real-time regex and NER scanning redacts API keys, credit cards, BVNs, and personal identifiers before leaving the boundary.

Inference pipelines can deploy within private customer VPCs (AWS, GCP, Azure) without external telemetry phone-homes.

Inference routing falls back automatically across frontier models (Claude, OpenAI, Gemini, local vLLM) in < 250ms on provider downtime.

Per-user and per-workflow token quotas enforce hard budget boundaries and alert on runaway looping agents.

Critical financial, legal, and diagnostic workflows pin model parameters to ensure reproducible outputs.

Every prompt, completion, tool call, and source citation is sealed into an immutable append-only hash chain.

Database administrators cannot alter historical AI decisions without cryptographically breaking the ledger verification.

One-click export of cryptographically signed compliance audit packages formatted for formal banking, NDPR, GDPR, and ISO auditors.

Generated answers require verified source citations with exact document hashes; unsupported statements are suppressed.

Autonomous agent mutations require explicit transactional rollback hooks or human-in-the-loop approval gates.

Secondary validation layers inspect both raw inputs and indirect document payloads for injection payloads.

P95 response latency is monitored continuously with automated alerting on degraded retrieval or synthesis.

Documented 15-minute emergency severance protocol to sever external model access without disrupting core database systems.

Executed Data Processing Addendum and 24/7 designated engineering escalation channel for contracted tiers.