[ GUIDE / 8 MIN READ ]

    What is AI production readiness?

    By Alex Cinovoj, Founder & CTO, TechTide AI. 13 years of mixed IT, last 2 focused on AI implementation.

    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 audits readiness in 48 hours

    The $1,000 AI Production Readiness Audit runs the six-pillar checklist against your codebase, prompts, MCP servers, retrieval layer, and observability stack. Output: a written go/no-go memo, ranked fix list, and a clear recommendation for the next step (Production Triage, Workflow Rescue, or "do not ship"). 48 hours from intake to deliverable.

    Frequently asked

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

    Most stuck pilots get to production in 21 days through a Workflow Rescue engagement, scoped to one workflow. Pilots with a soft foundation may need a Production Triage first.

    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 audit usually shortens the path by 2-3 months.

    Do we need all six pillars before launch?+

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

    Stuck on AI? Start with the $1,000 AI Audit.

    48 hours. Clear go/no-go on whether your pilot ships. Run by Alex Cinovoj directly.

    See the $1,000 AI Audit →Talk to Alex