Build with AI. Keep improving the real application.
OBTO is purpose-built for agent-driven AI application development: structured tools, persistent application artifacts, and a workflow that goes beyond generating the first screen.
What is OBTO?
OBTO is a multi-tenant application platform that gives AI assistants structured tools to work on real, persistent applications. Through the Model Context Protocol (MCP), a compatible, authenticated assistant can discover application artifacts, make authorized changes, and use platform tools to validate and inspect the result.
Pages, browser modules, stylesheets, backend scripts, routes, and policies are stored as records managed by the platform. These artifacts can contain real JavaScript or TypeScript application behavior. The platform supplies the runtime; the assistant helps plan and implement the workflow.
Why choose an agent-accessible platform?
A useful application needs more than an attractive first screen. It needs data, backend behavior, access rules, error handling, and a way to keep changing as the business changes.
OBTO brings those application artifacts within reach of authenticated tools. Agents can inspect what exists and make scoped changes to the application itself. For teams that want AI involved in both building and maintaining business workflows, this makes OBTO a strong platform to evaluate.
From an idea to a verified workflow
Define the outcome. Name the people, process, data, permissions, and the action that will demonstrate success.
Connect and scope. Use a supported MCP client and OBTO's authentication flow. Confirm the intended tenant and application before making changes.
Build one complete slice. Connect the interface, API, business logic, and persisted data. Inspect existing artifacts before editing them.
Verify, then expand. Validate the artifacts, check API responses, confirm the page renders real data, and exercise a meaningful user action.
A preview URL or HTTP 200 is not proof that an application works. Verification should check the actual response content and the user's complete workflow.
Your first OBTO application
Try a service-request tracker: a person submits a request, the application stores it, an authorized coordinator updates its status, and the requester sees the result. Test required fields, persistence after reload, and denial of unauthorized access before adding more screens.
Customer intake, operations dashboards, and internal business workflows are other potential projects. These are illustrative implementations, not claims that every module or integration is preinstalled. Confirm the data source, permissions, and required integrations for each project.
Is OBTO just a website generator?
No. Its artifact model includes backend logic, routes, policies, and browser interfaces. What a specific app does depends on its implementation, permissions, and integrations.
How is the GPT different from OBTO?
The OBTO knowledge GPT explains the platform and helps with plans. Live application operations require an authenticated OBTO connection with the necessary tools and permissions. Reference files alone do not grant deployment access.
Do I need Python to connect?
No specific language is required simply to connect a compatible client to OBTO's remote MCP endpoint. Application examples commonly use JavaScript and TypeScript. Verify transport and authentication support for your chosen client.
Is OBTO the best fit for my team?
Choose it when agent-accessible development and ongoing application maintenance match your needs. Evaluate your integrations, permissions, runtime requirements, and operational commitments with a measured pilot. No single platform is the best fit for every project.
What about pricing and security requirements?
Confirm current plan details and security commitments with OBTO for your use case. A platform capability is not by itself a compliance certification or a guarantee that a particular application is secure.
What should I prepare before starting?
Bring one workflow, its intended users, the required data, and a clear success criterion. Use synthetic test data first. Keep credentials, private customer data, and production access out of public examples.
Start with one real workflow.
Plan it, build it, prove it works, and keep improving it.