How long does it take to get a Claude pilot production-ready?

It depends on the workflow and what the review finds. AI Production Systems scopes a bounded implementation to one workflow once the decision justifies it.

What's the difference between production-ready and 'works in demo'?

A demo runs once with a known-good input. Production runs continuously with adversarial inputs, real users, real cost, and real consequences when it breaks. Production readiness is the gap.

Can we make our pilot production-ready in-house?

Yes, when the team has shipped at least one production AI system before. When this is the first one, an outside review can help clarify the path.

Do we need all six pillars before launch?

Yes for production. Skipping any one is how pilots end up in incident reports. The review ranks which pillars are weakest so you know what to fix first.

GUIDE / 8 MIN READ

What is AI production readiness?

By Alex Cinovoj, Founder & CTO, TechTide AI · 13 years in US enterprise IT.

A Claude pilot that works in a demo is not production-ready. Production readiness is the gap between "the model returned the right answer once" and "this system is safe, observable, reversible, and cheap enough to keep running for 12 months." Most stuck AI pilots fail this gap silently, then loudly.

The production-readiness bar in one paragraph

An AI workflow is production-ready when (1) you have a regression eval suite that catches model and prompt drift, (2) every tool call is scoped, audited, and reversible, (3) the auth boundary sits in code you own and not in a prompt, (4) cost and latency have hard ceilings with fallback paths, (5) the system is wired to your existing observability stack, and (6) a runbook documents rollout, rollback, and on-call escalation. Anything less is a demo with risk.

The six pillars

Evals. A regression suite of 20-50 cases representing the workflow's actual job. Re-runs in CI weekly. Tracks pass rate over time.
Scoped tools. Every MCP tool has typed inputs, typed outputs, scoped OAuth, kill switch, and a reviewer queue for side effects.
Auth boundary. The model never holds raw credentials. A thin server you own enforces row-level security.
Cost and latency caps. Per-session and per-day token caps, fallback to cheaper models on context overflow, latency SLOs.
Observability. Every call logged with prompt, response, user, timestamp, latency, cost, piped to your stack (Datadog, Grafana, Honeycomb).
Runbook. Rollout, rollback, on-call escalation, named failure modes. Checked into the repo, owned by the engineering team.

Common failure modes we audit for

Prompt-as-auth. Permissions enforced in the system prompt instead of the server. Trivially bypassed.
Side-effect tools without review queues. A "write" tool that hits production on first call.
No eval suite. Team can't tell whether a prompt edit improved or regressed the workflow.
Open-ended cost. No per-session cap, no fallback model, one runaway loop spends $4K overnight.
Black-box logging. Logs go to a vendor dashboard you can't query, can't alert on, can't export.
No rollback path. Rollout strategy assumes the new prompt always works.

How TechTide AI reviews readiness

The AI Production Systems Production Gate runs the six-pillar checklist against your codebase, prompts, MCP servers, retrieval layer, and observability stack. Output: a written ship, fix, redesign, or stop decision, ranked fix list, and a bounded implementation path when the decision justifies moving forward.

Frequently asked

It depends on the workflow and what the review finds. AI Production Systems scopes a bounded implementation to one workflow once the decision justifies it.

Bring one system. Leave with a decision you can defend.

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