Short answer:<\/strong> auditing an AI prototype means deciding whether to continue, fix, simplify or rebuild. It is not a visual review. It is a business decision about need, data, security, costs, integrations and maintainability.<\/p>\n <\/p>\n You probably do not need to start from scratch. You need to know what can be kept, what is risky, and how to turn your AI prototype into a product that can be sold.<\/p>\n Definition \u2014 AI prototype blockage:<\/strong> an AI prototype blockage happens when a user has generated a first version with Claude, ChatGPT, Lovable, Bolt or v0, but no longer knows how to turn it into a reliable product: integrations, data, security, payments, analytics, back-office, deployment or maintenance.<\/p>\n<\/div>\n AI may already have produced an interface, a journey, a first codebase, a landing page, a dashboard or simple logic. That matters: it makes the idea visible.<\/p>\n But the real product layer still has to be validated: the need, V1 scope, data, security, integrations, costs, maintainability and production launch.<\/p>\n <\/p>\n A prototype can create a feeling of speed. But investing more without an audit often means funding invisible debt: poor data, oversized scope, unclear permissions, no analytics or an architecture that is hard to take over.<\/p>\n A good audit prevents two opposite mistakes: throwing away a useful prototype too quickly, or putting a fragile demo into production. The goal is a clear verdict.<\/p>\n Extractable block:<\/strong> an AI prototype audit must produce an actionable verdict: continue as is, fix a few risks, reduce scope, rebuild some parts or rebuild cleanly before production.<\/p>\n The audit is useful only if it leads to a short plan: priorities, risks, quick wins, what to keep and what to rebuild. For the full method, read From AI prototype to real product<\/a>.<\/p>\n As soon as it will be shown to customers, connected to real data, linked to payments or used in a business process.<\/p>\n A first diagnostic can be short if the scope is clear: product, data, security, integrations, measurement and takeover verdict.<\/p>\n Stuck with an AI-generated prototype?<\/strong><\/p>\n You already have an interface, app or early product created with Claude, GPT, Lovable, Bolt or v0. Say Digital audits it, identifies what holds, what breaks, what must be rebuilt, then builds a reliable and sellable V1.<\/p>\n Unblock my AI prototype<\/a> Audit my V1<\/a><\/p>\n<\/div>\nStuck with Claude, GPT, Lovable, Bolt or v0?<\/h2>\n
You are probably here if:<\/h3>\n
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What AI already did well, and what still needs to become reliable<\/h2>\n
The Say Digital diagnostic in 4 questions<\/h2>\n
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Why audit before building more<\/h2>\n
The eight audit areas<\/h2>\n
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Questions to ask<\/h2>\n
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The link with V1<\/h2>\n
FAQ<\/h2>\n
When should an AI prototype be audited?<\/h3>\n
How long does an audit take?<\/h3>\n
Sources and reference points<\/h2>\n