Custom AI-Powered Apps
AI SaaS MVP development for founders and companies turning an idea into a real product with accounts, billing and one AI workflow that works.
What you get
Building an AI product prototype has never been easier. A weekend with an LLM API and a template can produce an impressive demo. Turning that demo into something strangers will pay for is a different job. You need sign-up, billing, data isolation between customers, error handling when the model returns something odd, and a way to know whether the AI is actually getting better or worse.
Our AI SaaS MVP development service is for founders, domain experts and established companies launching a new product line. We help you cut the idea down to one core workflow that solves a real problem, then build it properly: a production web app with accounts, subscriptions, an admin area and an AI feature that has been tested against real examples.
We know the unglamorous parts of running software because we do it ourselves. Synova sells its own Shopify apps and WordPress plugins, which means handling licensing, recurring billing, webhooks, upgrades and support for real customers. That experience shapes how we build your MVP: so it can survive its first hundred users, not just its launch day.
The problem
Most AI product ideas stall in one of two places. Either they never get past a prototype that only the founder can run, or they try to launch with ten features and run out of time and money before anyone uses them.
The common problems we see:
An MVP should answer one question: will the right customers use this and pay for it? Everything we build is aimed at getting you to that answer quickly, with a product solid enough that you don't have to throw it away if the answer is yes.
What we build
The feature that makes your product worth paying for, such as analyzing documents, generating reports or answering questions over a customer's data, tested against real examples.
Sign-up, login, password reset, Google sign-in, team invites and roles, with each customer's data kept strictly separate.
Stripe plans, free trials, usage limits tied to plans, invoices and a self-serve billing portal, with webhooks handled reliably.
An internal dashboard to see customers, usage and errors, impersonate a user for support, and switch features on per account.
Token usage and AI costs recorded per customer, so you can see margins by plan and set fair limits before costs surprise you.
A guided first-run experience plus product analytics on key steps, so you know where new users get stuck and what they actually use.
Scenarios
A former property manager wants software that reads lease agreements and flags risky clauses for small landlords. The MVP handles upload, extraction, a clause checklist and a shareable report, on a monthly subscription.
A consultancy built a proposal-drafting workflow for its own team and other firms want it. We rebuild it as a multi-tenant SaaS with team accounts, templates per firm and Stripe billing.
A marketing specialist wants to sell automated monthly performance reports to local agencies. The MVP connects to ad platforms, generates a written summary with an LLM and lets agencies white-label the PDF.
An industry association wants members to search years of guidance documents in plain language. The MVP adds member login, AI search with source citations and an admin area to upload new documents.
How we build it
We work in three phases, each with a clear decision point.
We agree who the first customer is, what job they're hiring the product to do, and what the smallest version looks like. We test the AI part early on real sample data, because that's where the biggest technical risk usually sits. You get a clickable prototype to show potential customers.
We build the production app on a stack you can hire for: Next.js, TypeScript, Postgres and Stripe, deployed on Vercel or AWS. Each AI feature gets an evaluation set of real inputs and expected outputs, so changes to prompts or models are measured, not guessed. You see working software on staging every week or two.
We help with a soft launch to early users, watch the error logs and usage data closely, and fix what real customers find. Then we plan the next set of features based on what people actually do, not what was on the original wish list.
If your product is mainly a conversational assistant, our AI agents and chatbots team works on that side. If you're adding AI to a product that already exists, LLM integration is likely a better fit than a new MVP. Our process page covers how we handle scope changes along the way.
Most of the MVPs we'd take on run 8–14 weeks from scoping to launch, depending on the core workflow and integrations. We keep the first version narrow on purpose, because shipping sooner gets you real feedback sooner.
Yes, fully. The code is in your GitHub organization, and hosting, Stripe, AI provider and domain accounts are in your company's name. We don't take equity or keep any rights to the product.
Yes, and it's one of the most valuable parts of the work. We push back on features that don't help you test whether customers will pay, and we flag where the AI part is risky so you can test it before committing.
We track token usage per customer from the start, use smaller and cheaper models for simple steps, cache results where it's safe, and set plan limits. That gives you the numbers to price your plans sensibly.
It will sometimes, so we design for it. That means showing sources, letting users edit outputs, flagging low-confidence results and logging everything. For high-stakes outputs we recommend a human review step in the workflow.
Yes. Many founders keep us on for the next phases, while others hire their own developers. We document the codebase and do a proper handover either way.
Tell us who it's for, what problem it solves and what you've built or validated so far. We'll suggest a realistic first version and what it would take to launch.
We pick tools for your situation, not ours. These are the ones we reach for most on this kind of work.
We don't just build for clients. These are our own products, used by real stores. See all products.
The sales rep portal Shopify B2B is missing — reps order at contracted pricing, and commissions calculate themselves.
Product registration, serial numbers, and warranty claims — one app, fully automated on Shopify.
A practical framework for deciding whether to buy another SaaS product, configure a low-code tool, or build a custom internal tool, including the costs people forget on both sides.
An honest breakdown of what makes a custom AI app cost what it does, how long each phase usually takes, and the running costs that continue after launch.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.