For a startup or a new business line the risk is not the technology: it is spending six months building something the market does not want. An MVP is how you find out early, with AI in the foundations rather than added at the end.
The idea is clear and the market seems to be there, but there is nobody to build it. Hiring a team takes months, a traditional agency proposes a year of development, and a weekend prototype does not survive its first hundred users.
With AI there is an extra risk: building the product around a model and its current limits. In six months there will be a better or cheaper one, and the product has to be able to use it without being rewritten.
One week to decide what the first paying user really needs. Everything else goes on a list, not into the code.
Login, payments, roles, email, logs. Nobody notices them until they break, and a prototype usually skips them.
Prompts, evaluations and model calls live in a separate layer. Changing provider is a configuration, not a project.
Cost per user, answer quality, feature usage. The next investment is decided on what users actually do.
These are the numbers we set in discovery, before writing any code. The starting value is measured on your data, and the prototype is judged against it.
From the start to the moment someone outside the team uses the product. It is the number that decides how much a mistake costs.
How much the product spends on AI calls for each active customer. If it exceeds what the customer pays, the business model needs rethinking early.
Which features are used and which are ignored. It is the data that says where to invest in the next version.
If you have not yet discussed the idea with a single potential customer, an MVP is premature. A few interviews and a clickable prototype cost far less and answer the same questions.
ConSergio.ai was born from the needs of a real hospitality property and became a product with customers of its own.
How we built itClosed tickets, commits and pull requests become the end-of-sprint report and the release notes, in two versions: technical for the team, readable for clients and users.
Application logs and databases already show how people use them, but nobody has time to read them. AI reads them, writes a report and suggests what to improve.
A free 30-minute call, with no commercial follow-up unless you want it.