Demo to production is a checklist, not a mood. At GenOrbit we walk this list before every AI launch so go-live day is boring in the best way — predictable, reversible, and observable. Go-live day should be the most boring meeting of the quarter — if excitement means surprises, the checklist was skipped.
Security and tenancy
Authenticate every route. Enforce tenant isolation in vector indexes, caches, and object storage. Scrub PII from logs unless compliance mandates full audit with retention policies. Rotate API keys through a secrets manager — never commit provider tokens. Pen-test auth bypass on tenant isolation — attempt cross-tenant retrieval in staging with two test accounts. Document data retention for prompts, logs, and embeddings in customer-facing DPA language before sales closes enterprise deals.
Rate-limit by plan tier and IP for public endpoints. Validate tool arguments server-side before execution — models hallucinate parameters under pressure. Document data retention for prompts and embeddings in customer-facing policies. Rotate secrets automatically where possible; manual rotation fails during holiday on-call gaps. Validate tool argument schemas reject out-of-range values before any write operation touches production databases.
Reliability and operations
Health checks, autoscaling, queue backpressure, and circuit breakers on provider calls. Feature flags for prompts and models enable rollback without redeploying the entire API. Backup vector indexes and test restore — index corruption happens. Load-test the query path at two times expected launch traffic — ingestion backpressure should not starve query workers on shared hosts. Document RTO and RPO for vector index restore; test restore quarterly, not only at launch.
Timeouts on every external call — users abandon after eight to twelve seconds of silence. Degraded mode with fallback models beats hard failures during provider incidents. Degraded mode should return cached answers or explicit service-unavailable messaging — silent quality collapse is worse than honest downtime. Feature flags per tenant allow rollback for one customer without global revert.
Quality gates
Golden-set eval must pass in CI before promote to production. Canary five percent of traffic when changing retrieval or prompts. Schedule post-launch reviews at forty-eight hours and two weeks. Golden-set eval in CI must match production prompt and index versions — staging-only eval gives false confidence. Canary analysis compares faithfulness and cost between control and treatment cohorts for at least twenty-four hours.
Runbooks cover model outage, embedding migration, and spike in toxic outputs. Cross-link to how we test and deploy AI systems for the full engineering narrative. Post-launch reviews at forty-eight hours catch integration bugs; two-week reviews catch usage pattern surprises. Link runbooks from the on-call pager — engineers at 3 AM should not search Notion for index restore steps.
What to do next
Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.
Related: /services/custom-software-development · /blog/how-we-test-deploy-ai-systems · /blog/production-llm-api-fastapi-openai