It asks
The guided interview turns your sentences into a structure, asks several questions at once and proposes answers. Where your brief is silent, it asks instead of inventing a rule.
AI
AI proposes. People decide. The app records.
Reventless uses AI at every step from your first sentence to the running app, and checks everything it produces against rules you agreed. It does not invent your business.
Where AI helps today
In the authoring tools, phase by phase. You accept or correct every proposal.
It drafts
It drafts how the app splits into parts and what each cut costs, and offers ideas you did not ask for. You choose.
It makes it work
It writes the logic of each decision until your own examples pass as tests, and stops to ask where they disagree.
It shows what a change touches
It proposes the new sentences of a change and shows what else it affects before anything is built.
It asks instead of inventing
The first worry about AI is that it makes things up. The tools are built so that it does not have to: where a description leaves a rule open, the tool asks the person who knows. When the first version of an online shop was built from its written brief alone, the brief did not say how an order is placed, and the tool asked rather than guessing.
What is recorded
Every decision your app makes is kept as a permanent record, and so is every change to its description. The result can always be traced back to a sentence someone agreed to.
Why it does not need the largest models
A frontier model is needed when the AI must know everything and may answer anything. Reventless is built so that it needs neither. Each guardrail takes work away from the model.
A small, closed vocabulary
There is one way to say what can be asked, decided, recorded and shown. The AI fills in a form whose shape is fixed instead of inventing an architecture.
A strongly typed specification
Every field, every event and every case has a declared type. A wrong value is not a judgement call but an error.
A very fast compiler
A change is checked in a fraction of a second. A decision that forgets a case is refused at once, inside the AI’s own loop, so it can simply try again.
Your examples as tests
Your given-when-then examples run as tests, and the AI’s code is accepted only when they pass. The tests judge, not the model.
Answers held to a schema
Inside an app, an AI step fills slots or picks from a list, and the model server can make a malformed answer impossible.
Narrow tasks
Each step is one fixed instruction, and where the description is silent the tool asks a person.
Together they turn “write correct business software” into “fill in a typed form and keep correcting it until the compiler and the examples agree”. An AI that is checked can be a smaller AI. That is also the difference to vibe coding.
An assistant that works with your running app
Every Reventless app offers its commands and views over the open Model Context Protocol, beside its regular interface. An AI assistant connected to it can look at real data and carry out real actions, with exactly the rights of the person using it, under the same rules as a person. This is built today.
Planned: AI on a model you host yourself
Reventless will run its AI on a model you host: on a laptop for development and workshops, on your own servers or sovereign cluster in production.
Your data stays with you
With a model you host, nothing about your business, your people or your customers leaves your machines.
A cost that does not grow with use
A machine a month, or a laptop you already have, instead of a bill per message.
A model that does not change under you
A model you host stays as it was tested; a provider’s model changes every few months.
Working offline
On a laptop the whole app, AI included, can run with the network switched off.
Planned: AI steps inside your process
Steps a model does for you
Reading a fault report, sorting a request, extracting fields from a regular document, each answer recorded like any other decision, with a person confirming above a threshold you set.
An assistant for your staff
A conversation built into the app for the people who use it, bounded by their own rights.
What is not yet true
- Building an app runs on a large cloud model today. Whether a model you host is enough for that work too is what the guardrails make likely and what we will measure before we say it.
- Inside an app, small models you host do well at classifying, extracting from regular documents and search. Drafting text, answering open questions and proposing actions still need a larger model.
- A model good enough for production needs a graphics card on your servers. Local AI saves the bill per use, not every cost.
Find out if Reventless fits
A short conversation is enough to see whether your business process and Reventless are a match — no obligation.
Free 30-minute intro call — no obligation.