AI MVP development, starting with one job the model does well.
AI MVP development means shipping a real product built around one job a language model does reliably, with login, billing and cost limits around it. Bespoke Devs builds these on Claude, OpenAI or Gemini models. If the scope fits, it’s $4,997 in 15 days. Heavier AI work gets a custom quote. You own all the code at launch, and we’ve shipped AI products of our own.
Reviewed by Anubhav Gupta, Bespoke Devs · Updated
What AI MVP development includes
You get a complete product, with AI at the centre. The model is often the smaller part of the work.
- One well-defined AI featureGeneration, summaries, extraction, classification or chat over your data. One job, done reliably.
- The product around itUser accounts, Stripe billing, a dashboard and an admin panel, so customers can pay from launch.
- Usage limitsPer-user caps, so that one heavy user or a stuck loop can’t run up your bill.
- Handling for bad outputModels will sometimes be wrong. The product needs a way for users to check, edit or retry.
- Prompt and output loggingSo you can see what’s failing, with care taken over what you store.
- Launch basicsA crawlable marketing site, schema, a sitemap and analytics. See SEO for SaaS.
AI products we’ve shipped
These are published results from our work page. Two of them are our own products.
- $15K
- MRR within 90 days of launchBlogStorm: AI content planning for agencies →
- 500+
- paying customers in 6 monthsMapPack Toolbox: local SEO suite with AI entity analysis →
- 1,000+
- active users, our own productWPRise: AI WordPress site builder →
- Thousands
- of monthly users, our own productTootler: AI SOP and visa document generator →
How we build one
It follows our usual 15-day shape, with extra care on the AI parts.
- Day 1–2Scope the jobWe pick the one thing the model must do, what good output looks like, and what happens when it’s wrong.
- Day 3–5DesignScreens in Figma, including waiting states, errors and editing. AI features spend a lot of time in those states.
- Day 6–12BuildProduct, model integration, limits and billing. We use sample inputs to compare models for your job.
- Day 13–14TestQA across devices and browsers, plus awkward inputs on purpose.
- Day 15ShipLive on your domain, with monitoring set up.
What AI MVP development costs
Build cost and running cost are separate. Model usage is a running cost on top, so estimate your monthly LLM API cost before you set your prices.
- One AI feature inside a full product
- User authentication
- Stripe billing
- Admin dashboard
- SEO-ready launch
- 60–90 days of support
- Agents and automation
- RAG over your own data at scale
- Custom architecture
- Dedicated team
Does yours fit the fixed price?
It may not. Our cost calculator adds 48 hours for AI features and a 1.15 multiplier for AI products, because they need more testing. A typical AI product with login, billing, admin and no existing designs comes to about 366 hours, above the 300-hour limit for the fixed price.
There are ways to bring it in. Bringing your own Figma file removes 60 hours before the multiplier, which puts that example at about 297. A simpler admin panel helps too. If it still doesn’t fit, we’ll give you a custom quote, and you can decide if it’s still the right time.
What kind of AI product are you building?
Complexity goes up quickly as you move down this table. Most first versions should stay near the top.
| Type | Example | Main risk | Start here? |
|---|---|---|---|
| Single call | Summarise, rewrite, extract fields | Inconsistent output | Yes |
| Chat with context | Assistant inside your product | Cost per conversation, wrong answers | Often |
| RAG over your data | Answers from a customer’s documents | Retrieving the wrong content, leaking data between customers | If the data is the product |
| Agents | The model takes actions in other systems | Doing the wrong thing with real effect | Rarely |
| Custom or fine-tuned models | Your own trained model | Cost and time, often for little gain early on | Almost never |
The risks that matter most
The OWASP Top 10 for LLM Applications is a good checklist. These four come up first in a typical MVP.
- Prompt injectionUser text or a document can carry instructions the model follows. Don’t give the model more power than the user has.
- Sensitive information disclosureKeep one customer’s data out of another’s answers, and be careful what goes in prompts and logs.
- Excessive agencyIf the model can send, delete or pay, put a person or a hard rule in front of that action.
- Unbounded consumptionWithout limits, one user can run up your model bill. Caps belong in the first version.
Models and stack
We use models from Claude, OpenAI and Gemini, and choose based on the job. Published prices for comparable tiers can be very close, so price alone rarely decides it. Test with your own examples, and keep the model behind a thin layer so that you can switch later.
The rest is our normal stack: typically Next.js or React, Node.js or Python, PostgreSQL or Supabase, Stripe, and AWS or Vercel. You own the code and the accounts.
If you’ve got a product already and just want to add a model to it, read how to integrate AI into SaaS.
When we’re the wrong choice
We’re an agency, so weigh our view accordingly.
- You need research-level machine learning or your own trained models. Hire ML specialists.
- You’re in a regulated field and the model’s output has legal or medical effect. That needs specialist review we don’t include.
- You haven’t tested whether a model can do the job. Try it yourself in a playground with 20 real examples first. It’s nearly free.
- You want an AI MVP builder tool to make it yourself. Try [an AI app builder](/saas-mvp-development/vs-ai-app-builders) first, because that’s a cheaper way to learn.
Common questions
The API call is usually the easy part. The work is the product around it: accounts, billing, usage limits, handling wrong answers, and data separation.
Output isn’t predictable, costs rise with usage, and security has new failure modes such as prompt injection. So it needs more testing and limits than a normal feature.
It depends on how many requests you handle and how long they are. A simple feature can cost a few dollars a month or hundreds. See our [worked example](/ai-mvp-development/questions/what-do-llm-features-cost).
Check each provider’s current terms. Anthropic’s privacy centre says that, by default, it doesn’t use inputs or outputs from commercial products for training. Check OpenAI’s and Google’s current terms directly.
No. Nobody can honestly promise that for a language model. We design for it being wrong sometimes, and test with examples from your field.
Book a free 20-minute call. We’ll scope it live, and if it’s a fit, you’ll get a written proposal within 24 hours.
Go deeper
Tell us what you’re building.
Takes under a minute. We come back with a scope and a fixed price.
- Reply within 24 hoursA real person reads every brief.
- Fixed scope, fixed priceYou get a written proposal, no hourly billing.
- You own the codeFull ownership of code and assets at launch.