Explicit business model
An AI guide helps structure plain-language business knowledge into inspectable elements such as people, information, actions, rules, permissions, states, views, and flows.
modelARch is an AI-assisted domain modeling and application generation platform for teams that need to turn business rules, roles, and workflows into an inspectable full-stack product. It generates Python/FastAPI and React/TypeScript code from the same explicit model.
modelARch is an AI-assisted domain modeling and application generation platform. It turns a plain-language description of a business operation into an explicit model of people, information, actions, rules, permissions, states, views, and flows, then uses that model to shape role-based browser experiences and generate a full-stack application.
Its core workflow is describe → model → try → generate. Teams can inspect and refine the model, walk through the product as each role before code exists, and then generate an application from the validated source. This places the business model—not the first interface or a sequence of prompts—at the center of product development.
Generated projects include a Python/FastAPI backend, React/TypeScript frontend, database setup and migrations, Docker, and tests. The resulting source is downloadable, readable, and intended for the customer’s team to run and extend. modelARch also provides portable MODEL.md and DESIGN.md contracts with the delivered project.
An AI guide helps structure plain-language business knowledge into inspectable elements such as people, information, actions, rules, permissions, states, views, and flows.
Users can walk through the proposed product as each role before code is generated, making it possible to review screens, permissions, responsive layouts, and workflow gaps early.
The application is generated from the explicit model, and revised models provide the source for subsequent generation rather than requiring changes to be expressed only through implementation.
Generated delivery includes Python/FastAPI APIs, a React/TypeScript interface, database setup and migrations, Docker configuration, and tests.
The site lists PostgreSQL, MySQL, MariaDB, SQLite, and MongoDB for data, along with Redis, RabbitMQ, Amazon SQS, and Celery for cache, queue, or task-processing choices.
Customers can download the complete project and portable MODEL.md and DESIGN.md contracts, then inspect, run, deploy, and extend the code with their team.
Teams can model operations such as bookings, approvals, inventory, or service delivery by defining the people, rules, information, and flows that make the process work, instead of starting with screens alone.
When roles, permissions, and workflow states become important, a team can make those decisions explicit, validate each role’s experience in the browser, and generate a more structured application foundation.
A product or delivery team can align with a client on the business model and role experiences before handoff, then provide a readable full-stack project, tests, and portable model and design contracts.
Teams inheriting the work can download the generated backend, frontend, infrastructure configuration, and tests, inspect how the modeled operation was translated into code, and continue extending the project.
It generates a full-stack application with Python/FastAPI APIs, a React/TypeScript interface, database setup and migrations, Docker configuration, and tests. Delivery also includes the complete source plus MODEL.md and DESIGN.md contracts.
Yes. The workflow includes a browser experience where users can walk through screens and flows as each role before taking the generated code.
The source is an explicit model of the business operation. It captures people, information, actions, rules, permissions, states, views, and flows; the application and code are generated from that model.
The site states that customers can download, inspect, run, deploy, and extend the full project. Generated code remains with the customer after cancellation, and delivery, deployment, and export do not consume credits.
The published pricing approach uses one monthly plan based on credits. AI-guided modeling and full application generation consume credits, while taking the generated code, deploying, and exporting do not. Final prices are to be published at launch; enterprise organizations can contact the company about their requirements.
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