Tepia builds AI MVPs for startups that need a working product in front of real users, not a demo. Builds typically pair React Native or React with a Node.js or Python backend and OpenAI or Anthropic model APIs behind a provider layer you can switch, with feedback capture from day one and development and QA typically taking 2 to 6 months.
An AI MVP has to prove two things at once: that the model does the job and that people come back to use it.
Tepia routes model calls through a thin layer you own, so moving between OpenAI, Anthropic or a hosted open model is a config change.
Read: MVP App Development for Startups
Per user limits, abuse checks and model routing keep a viral week or one very heavy user from overwhelming the product.
Read: App Development for Startups
Every AI response in the MVP can be rated, edited or rejected, and Tepia stores those signals as the start of your own dataset.
Read: How to Plan an MVP That Proves Your Idea
Tepia plans for App Store and Google Play review of AI features, including content reporting and clear labeling of generated output.
Industry figures Tepia plans around when scoping AI MVPs for startups.
CB Insights found 43 percent of venture backed startups that shut down since 2023 cited poor product market fit as a cause.
Pew Research Center reports about 91 percent of US adults own a smartphone, so most AI MVPs need a mobile path.
Tepia clients report apps averaging 4.5 stars on the App Store and Google Play, a signal early adopters notice.
A conventional MVP tests whether people want a workflow. An AI MVP tests that and a second unknown: whether the model output is good enough, often enough, that users trust it. A note taking app with a perfect interface still fails if its summaries miss the one detail that mattered, and a brilliant model fails if the product around it is confusing. Tepia scopes AI MVPs so both risks get tested with real users in the same release.
There are also problems ordinary MVPs never face. Model output varies from one request to the next, so QA cannot rely on fixed expected strings. Each request has a variable delay, which shapes the interface. Model providers change versions underneath you. And investors will ask what stops a competitor from copying your product with the same API in a weekend. Planning for those in the first release saves a rebuild later.
If your product does not depend on AI to deliver its core value, a standard build may serve you better. Our MVP app development page covers that path.
Tepia runs a scoping exercise that sorts every proposed feature into must have, later and not in this product yet. AI features get extra scrutiny because each one adds evaluation and support work. A typical result for a consumer or small business AI product looks like this:
| Capability | In the MVP | Reasoning |
|---|---|---|
| The single AI task your pitch is built on | Yes | This is the hypothesis, and everything else supports it |
| Rate, edit or reject on every AI output | Yes | Users correct the model and you collect training signal |
| Accounts, onboarding and saved history | Yes | Without history you cannot measure return usage |
| Provider layer and usage limits | Yes | Protects you from outages, model changes and runaway usage |
| Analytics on activation and repeat use | Yes | Investors and founders need retention numbers, not anecdotes |
| Second and third AI features | Later | Each adds evaluation work before you know the first one lands |
| Fine tuning a custom model | Later | Rarely worth it before you have real usage data to tune on |
| Team workspaces and admin roles | Later | Add when paying teams ask for them |
| Fully autonomous actions on user accounts | Not in version one | Requires more trust than a new product has earned |
The value of the table is the conversation it forces. Founders usually arrive with three AI features. Most MVPs ship one, done well, with the others waiting on evidence. For the planning steps in more detail, read how to plan an MVP that proves your idea.
The fair criticism of many AI startups is that the product is a thin interface over a public model. Tepia helps founders build defensibility into the MVP itself, even when the first release uses an off the shelf model:
Tepia also keeps your prompts, evaluation sets and data in your own repositories and cloud accounts. You own the code and the learning, which matters when you raise a round or bring engineering in house.
Tepia uses the same six phases for a startup MVP as for an enterprise build, sized to a founder’s runway. A US based project manager works directly with the founding team.
Discovery typically runs 1 to 3 months, shorter when you arrive with designs, and development and QA 2 to 6 months. See our process and our work.
An AI MVP generates a different kind of evidence than a standard app, and Tepia instruments for it from the first build. Alongside sign ups and retention, you should be watching:
These numbers tell you whether to double down, change the model approach, or rethink the task. They also answer the questions investors ask after a demo. Tepia can continue as your support and app evolution partner after launch, or hand off to your own engineers with documentation. Compare options in how to choose an MVP development partner, then talk to Tepia about your idea.
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