The phrase 'production AI' exists because the AI industry built up a graveyard of prototypes that never reached real users. A model that achieves 95% accuracy on a curated test set in a Jupyter notebook is not production AI. Production AI is the same model, deployed behind an authenticated API, with input validation, output guardrails, monitoring on accuracy drift, cost ceilings, audit logging, a documented runbook, and a named on-call engineer when things break.
What separates production from prototype
Six dimensions: (1) reliability: uptime, latency budgets, graceful degradation under load; (2) security: authn/authz, prompt injection defense, sensitive-data filtering, secrets management; (3) observability: request tracing, cost per call, accuracy metrics, drift detection; (4) governance: model cards, eval harnesses, audit trails, version provenance; (5) cost discipline: caching, model tiering, prompt optimization; (6) operational ownership. Runbooks, on-call rotation, incident response, scheduled model refresh.
Why most enterprise AI projects fail to ship
The pattern is consistent: a successful internal demo gets approved for production, the data science team hands it to engineering, and the gaps in the six dimensions above surface during security review. Two weeks of polishing turns into two quarters of rebuilding. The right counter is to design for production from day one: pick architectures, tools, and team composition with the production bar in mind, not the demo bar.
How Prosigns approaches production AI
Every Prosigns AI engagement is scoped to ship to production with monitoring, governance, and an operations plan. We don't take research-only or pilot-only work. The first deliverable is a Production Definition of Done. What 'shipped' actually means, including the SLA, the eval criteria, the security posture, and the operational runbook. Everything else flows from that contract.