Free · no login · about 3 minutes
Your AI demo works.
Is it actually ready for production?
Fourteen questions across the engineering domains where AI builds actually fail — auth, authorization, secrets, cost controls, recovery, observability. Answer honestly (“not sure” counts) and get a straight classification, your top blockers, and the actions that close them. Your answers stay in your browser.
One line, your words. It leads the summary if you continue to the Builder — nothing else you answer here leaves this page.
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What “production-ready” actually means for an AI build
The distance between a working AI prototype and a dependable product is not more features — it is a set of engineering properties a demo never has to prove. This check assesses the fourteen that decide whether a build survives real users, real traffic, and a real bill:
Authentication & sessions
Broken session handling is the fastest way to lose user trust, and silent auth failures are the hardest class of bug to hear about from users.
Authorization boundaries
UI-only restrictions are not restrictions. Anyone with a browser console can call your API directly, and eventually someone will.
Secrets & sensitive data
A key in client code or git history is already public. Leaked model-provider keys additionally turn into someone else's usage on your bill.
Input validation & attack surface
Public endpoints receive hostile input as a matter of course. Unbounded input reaching a database, a model, or a filesystem is the classic path to compromise and runaway cost.
Tenant & customer isolation
Cross-tenant leakage is a business-ending class of defect: it converts every affected customer into a disclosure conversation.
Data & recovery
An untested backup is a hope, not a plan. Data loss without recovery is the failure users do not forgive.
AI failure handling
Provider incidents are routine, not exceptional. Without deliberate failure paths they surface as blank screens, infinite spinners, or corrupted output presented as truth.
AI cost & usage controls
Model spend is an unbounded liability by default. The first month with real traffic — or one retry loop — is where uncapped builds get expensive.
Error visibility
Whatever you cannot see, you cannot fix. Discovering outages from user complaints costs the trust the product exists to build.
Billing & entitlements
Entitlements enforced only in the client are honored only by honest users. Revenue leakage from unenforced limits is invisible until you look.
Deployment & environments
Testing against production data, or deploys that cannot be reversed, turn ordinary mistakes into incidents.
Tests & regression protection
Without regression protection every change is a gamble, and AI-generated code changes fast. Velocity without tests converts to fragility.
Approvals & audit trail
When an AI system acts on the real world, 'what happened and who approved it' is the first question customers, auditors, and incident reviews ask.
Operational ownership
A system only one person can operate — or that no one can — is a liability with an uptime number attached.
The classification you receive — PROTOTYPE, NEEDS HARDENING, PILOT-READY, or PRODUCTION CANDIDATE — is a readiness assessment built from your own answers. It is not a security certification, and where you answer “not sure” the honest output is verification work, not a guess. LYFYE builds and hardens production AI systems for a living; the LYFYE Builder is where a check becomes a conversation.
Frequently asked questions
What is the AI Build Reality Check?
A free self-assessment for anyone who has built software with AI — an app, an agent, an automation — and wants to know how far it is from production. You answer one question per engineering readiness domain and get a classification (PROTOTYPE, NEEDS HARDENING, PILOT-READY, or PRODUCTION CANDIDATE), your top blockers with why each one matters, what is already strong, and priority actions. It is a readiness assessment based on your answers, not a security certification or an audit.
Is an AI-generated prototype ready for production?
Usually not by default. A working demo proves the idea, but production readiness lives in the parts a demo never exercises: reliable authentication and sessions, server-side authorization, secrets handling, input validation, tested backups, AI provider failure handling, cost and usage controls, and error visibility. Those are exactly the domains this check assesses.
What does the check cover?
Fourteen readiness domains: authentication and sessions, authorization boundaries, secrets and sensitive data, input validation and attack surface, tenant isolation, data and recovery, AI failure handling, AI cost and usage controls, error visibility, billing and entitlements, deployment and environments, tests and regression protection, approvals and audit trail, and operational ownership.
Do I need an account or an email address?
No. The check runs entirely in your browser with no login and no email gate, and you see the full result first. Your individual answers never leave the page. Only if you choose to continue into the LYFYE Builder does a compact summary — which you preview and can edit — travel with you.
What happens after I get my result?
The result is yours to act on either way: it names your blockers and the priority actions that close them. If you want them closed by a team that does this for a living, one click carries your summary into the LYFYE Builder, and from there you can start a Build With LYFYE conversation about taking your build to production.