Slow responses
Users leave if they wait too long for an answer, so we stream responses where the product allows it.
An AI MVP tests one AI-powered job with real users before you invest in the full product. We build it in 2 weeks: one AI feature, sign-in, a database, and the screens around it. Because every call to a language model costs money, we keep rate limiting and proper authentication in even the smallest build, and during testing we check how many tokens each call uses and what it costs.
Written by Ganesh Ghatti · Founder, Squirrel Technologies
Updated
Building an AI MVP with us is priced on the same tiers as any MVP, from $3,999 to $8,999, depending mainly on your core feature. Once it is live, the AI model's usage is a separate monthly cost paid to the provider. That cost depends on how many people use the AI feature, how often, and how much text each call sends and returns.
Model costs grow with usage, so they need watching from the first test user. We rate limit both by IP address and by user, require proper sign-in, and measure token usage during QA so you know the cost per user before launch.
An AI feature can be slow, unpredictable, or expensive to run. Testing it with real users early shows whether people value it before you spend on custom infrastructure.
Users leave if they wait too long for an answer, so we stream responses where the product allows it.
Model output that the app cannot read breaks the screen. We validate responses and show a clear message when something fails.
Self-hosted or fine-tuned models can wait until users have shown they want the feature.
The 2-week build covers one AI job and what users need to reach it.
Technologies we use for this
The same 14-day process as every MVP, with model costs checked during testing.
Day 1
We agree the one AI job the MVP must do, look at sample inputs, and decide what the model must never do.
Days 2–3
We study your top two or three competitors, agree the pages and screens, finalise the user journey, and use AI to create a demo from your brand guidelines.
Days 4–9
Our developer builds the core features, sign-in, database, and the other parts the product needs, using AI coding tools such as Claude, Cursor, and Codex.
Day 10
We put the product on a server so people can use it online. A simple MVP does not need a CDN, queues, or microservices.
Days 11–14
People test the full journey, and we check how many tokens each call uses and what it costs. One or two rounds of changes, then launch.
Want the detail behind each day? Read Can You Really Build an MVP in Two Weeks?
Built an iOS and Android AI journaling app with React Native and Expo from the client's designs. Both stores approved it on the first attempt, and it was live 14 days after kickoff.
App Stores Deployed
Kickoff to Live on Both Stores
Attempt Approval: App Store & Google Play
Ganesh Ghatti, founder of Squirrel Technologies, walks through four practical techniques for reducing what an AI product spends on model calls. Useful once your AI MVP has users and the running cost starts to matter.
An AI MVP is a minimum viable product built around one AI-powered feature. Like any MVP, its job is to test whether your idea solves a real problem for real users. It also tests things only an AI product has to answer: whether the model's output is good enough, fast enough, and cheap enough per user.
We agree the one AI job the product must do on Day 1, design the screens around it on Days 2 and 3, build it with existing models on Days 4 to 9, deploy it on Day 10, and test it with people on Days 11 to 14, checking output quality and token costs as we go. Our developers also use AI coding tools such as Claude, Cursor, and Codex to build faster.
Our tiers ($3,999 to $8,999) cover building the MVP. AI model usage is a separate running cost, paid directly to the model provider, and it grows with the number of users and how often they use the AI feature. We measure the cost per call during testing so you know what each user costs before launch.
Usually 2 weeks when the AI feature uses existing models. If the product needs data collection and model training, it will likely take longer.
We rate limit by IP and by user, require proper sign-in, and measure token usage during testing so you know the cost per user before launch.
Yes. We can connect your documents or data through retrieval, so the AI answers from your content. We agree what data it can access during scoping.
Yes. We hand over the code and intellectual property after development, along with the access you need to keep running the product.
Ready to build an AI solution or digital product? Let's turn your vision into reality.
Fill out the form and we'll reply within 24 hours.
Share your idea and we'll come back with a quick assessment and a clear plan.
Follow us on Google
One click and our AI guides, case studies and build notes show up more often in your Top Stories and Google results.
Add as a preferred source