A successful AI demo proves that an idea can work. It does not prove that the business can trust it with real customers, sensitive data, or consequential actions.
Prototypes usually run with selected data, cooperative users, broad credentials, light traffic, and developers nearby. Production adds incomplete information, permission changes, unavailable APIs, model updates, malicious inputs, variable cost, and recovery under pressure.
Before launch, require evidence across five gates.
Define the workflow, its owner, the current baseline, and the result the AI must improve. “Build an onboarding assistant” is a feature request. “Reduce case-preparation time while improving document completeness and preserving human approval” is an operating objective.
Name the authoritative system for every critical fact and transaction. AI may interpret information or coordinate work, but state changes should pass through governed APIs or services. Retrieval must respect source ownership, freshness, versioning, and permissions.
Give each agent a distinct workload identity, least-privilege permissions, approved tools, transaction limits, short-lived credentials, and explicit approval rules. Prompt instructions are not access controls.
Test normal, ambiguous, adversarial, unauthorized, and dependency-failure cases. Verify the final business state independently. Define degraded operation, rollback, and human fallback before go-live.
Assign business and technical owners. Version model, prompt, retrieval, tool, and policy changes. Provide alerts, runbooks, support procedures, and an emergency shutdown path.
A prototype is ready only when the business can answer three questions with evidence:
Cayru can review one existing AI workflow, identify its go-live blockers, and implement the missing controls through senior AI Product Engineering, LLMOps / MLOps, modernization, and DevSecOps talent in Costa Rica.
Request a production-readiness review for one AI workflow.