1. Start from a problem, not the model
The first mistake is to start with "let's use AI". Start from a costly, frequent problem for a precise target. AI is only useful if it removes real pain: too much data entry, too much waiting, too much rare expertise.
2. Find the source data
The best AI startups rely on data that refreshes on its own (public data, business feeds, product behaviour). This source data is what keeps the product alive without manual work, and defensible against copies.
3. Build the minimal useful product
Aim for the smallest version that truly solves the problem end to end. A narrow but complete scope beats a broad but hollow demo. Reuse a shared technical foundation so you don't rewrite everything each time.
4. Choose local AI or an API
Many products combine both: local AI for volume and privacy, an API for peak-quality tasks.
| Criterion | Local AI | Cloud API |
|---|---|---|
| Usage cost | Fixed (your server) | Variable (per call) |
| Privacy | Data stays with you | Data with the third party |
| Setup | Slower | Immediate |
| Best for | Volume, sovereignty | Prototype, peak quality |
5. Plan for recurring revenue from the start
An AI startup that bills per task plateaus fast. Look for a structural reason to pay again every month: the data updates, the work is redone, the value renews. Recurring revenue (subscription) turns a tool into an asset.
6. Launch acquisition without a sales team
For a first product, favour channels that run on their own: long-tail SEO content, pages that answer a search exactly, presence in AI engines, and a shareable asset. The goal is a flow of qualified visitors without linear sales effort.
7. Keep sovereignty
Own your code, your data and your hosting. An AI startup whose core fully depends on a closed provider has neither margin nor defence the day prices or terms change. Sovereignty is not ideological, it is economic.
The method in practice
This is exactly the cycle INEYA applies to its AI ventures: real problem, source data, shared foundation, local AI by default, recurring revenue and self-hosting. Five products are already live and follow this method.
Frequently asked questions
Do you need to train your own model to launch an AI startup?
Rarely at first. Most AI startups combine existing models (local or via API) with their source data and product; training your own model comes later, if volume justifies it.
Local AI or cloud API to start?
A cloud API lets you prototype fast; local AI becomes attractive once volume, cost or privacy weigh in. Many products combine both.
How do you make an AI startup profitable?
By aiming for recurring revenue: data that refreshes and work that is redone give a structural reason to pay every month, which beats billing per task.