Build your own business systems
with Lovable

Lovable is an AI app builder: you describe the application you want in natural language, and it designs, codes, and hosts a working web application. No programming knowledge is required to get a functional first version.

For teams in Japan, this changes who can build. The person who actually runs the quoting process, the daily reports, or the approval flow can produce working software — instead of writing a requirements document and waiting on a vendor.

How large is it, by Lovable’s own figures? We have not verified them independently. In a blog post on 28 September 2026, Lovable said that more than 70 million projects had been created on the platform. That is a cumulative count of projects created. It does not say how many were published or are still in use. In its funding announcement of 12 August 2026, Lovable cited more than 900 million visits per month to apps built with Lovable (visits, not people) and said that employees at nearly two-thirds of US Fortune 500 companies use it. We read that as some use, not company-wide adoption. None of this describes Japan: we have not found a published survey of awareness or usage here, so we give no figure for Japan.

The bottleneck in most internal-system projects is translation: turning how the work actually happens into something a developer can implement. With Lovable you can explain the work, look at working screens, and correct course yourself, instead of waiting for a developer to interpret a document.

Iteration is cheap. When a screen does not match reality, you say so and ask for a revision. The try-and-fix cycle can happen while you are looking at the screen, instead of through a round of change requests.

Typical wins are the systems that packaged software never quite fits: order intake, customer management, quoting and invoicing, inspection records, internal knowledge bases, approval workflows. The kind of work that today lives in spreadsheets, email threads, and paper.

Marketing sites and landing pages work well too — but the deeper value for most companies is operational: purpose-built internal tools that match how your team actually works.

Start with one workflow that hurts, not a company-wide platform. Describe the work to Lovable as you would to a new colleague: who does what, in what order, and what must never go wrong. The skill that matters is not coding — it is articulating your own business.

Expect the first version to be wrong in interesting ways. That is the point: you now have something concrete to correct, on day one.

A working prototype is only the first step. Running an app also needs somewhere to keep data, a way to sign in, a public web address, a way to see who visits, and often email and payments. Lovable includes several of these. Lovable Cloud, its built-in backend, provides a database, sign-in and file storage. The same project can also use AI features inside the finished app, publish to your own domain, show visitor analytics, and send email from your own domain. Payments go through Paddle or Stripe, and online shops through Shopify. These are partner services, each with its own approval process, pricing and setup.

Email from your own domain needs a paid plan and DNS records added at your domain host, which in many companies is another team or a vendor. Lovable does not guarantee delivery to the inbox rather than the spam folder.

Lovable’s payments feature, which works through Paddle or Stripe, needs a paid Lovable plan (Pro or higher). On the free plan you can connect your own Stripe account instead. Real payments cannot be taken until the payment provider has verified your business, and that approval is the provider’s decision.

Shopify provides the shop’s checkout, inventory and shipping tools, and bills you separately for its own plan.

Two kinds of AI are easy to confuse. One helps you build the app. The other runs inside the finished app. They are billed separately, by usage.

Lovable Cloud lets you choose where data is stored (the Americas, Europe or Asia Pacific), and the choice cannot be changed later. Before you put personal data in an app, ask Lovable for its list of sub-processors and its security documentation.

The conditions and limits above follow Lovable’s documentation as of 21 October 2026. Features, plans and prices change, so check Lovable’s current pages before you decide.

Beyond the first version, design decisions start to matter: who can see which data, how data is organized, what happens when a part fails. A prototype that runs is not yet a system your company can rely on.

Claude Code (from Anthropic) and Codex (from OpenAI) are AI coding agents. Both offer cloud environments for working on code. Their official overview pages focus on that work and do not describe a built-in database, sign-in or hosting. What Lovable adds is that these components are in one place, which is meant to reduce the choices and integrations you handle yourself. Which tool is easier to use depends on the team and the job.

Many teams can and should build their first tools themselves — the learning compounds. Where Nihonbashi AI Lab helps is the part that needs judgment about your business: process design, data structure and permission design, how AI features fit the work, and the operating routines that keep a system alive after launch.

Based in Tokyo, we offer training for teams that want to build in-house, and hands-on delivery for systems that need to be production-grade from day one.

Lovable runs an official partner program with four tiers. If you plan to bring in outside help, knowing that the program exists lets you ask precise questions rather than take a firm at its word.

Three checks are usually enough. First, ask which tier the firm holds. Second, ask whether it has completed the program’s certification; building with Lovable is not the same as being certified in the program. Third, notice whether the conversation starts with your operations or with your screens. Screens are the fast part. Permissions, the data model, and the routines that keep a system running are where the work succeeds or fails.

Lovable also publishes a directory of partners, but listing follows certification, so a firm can be in the program without appearing there. Entries are self-reported, and Lovable says it does not vouch for their accuracy or recommend any firm. Treat the directory as one signal rather than as proof.

Nihonbashi AI Lab is based in Tokyo and is independent of Lovable Labs Inc. Apply the same three checks to us.