If you ask a consultant how to make your enterprise data "AI-Ready," they will likely give you a quote for $500,000 and a 12-month timeline. Their plan will involve:
- Extracting petabytes of data into a centralized Data Lake.
- Training or Fine-Tuning a custom model (at massive GPU cost).
- Building Robots (RPA) to click buttons on your legacy screens.
- Hiring Teams of data scientists to clean and tag the mess.
There is a faster, safer, and less expensive way. It creates an ontology that is instantly ready for both AI and human consumption, without moving a single byte of data.
The secret? Build a Database Web App.
The App Is The Ontology
In the Code On Time ecosystem, an application is not just a collection of screens. It is a Micro-Ontology—a self-contained, structured definition of a specific business domain (e.g., Sales, HR, Inventory).
When you use the App Studio to visually drag-and-drop fields, define lookup lists, and configure business rules, you are doing something profound:
- For Humans: You are building a high-quality, Touch UI application to manage the data.
- For AI: You are defining the Entities, Relationships, and Physics of that domain.
The platform automatically compiles your visual design into a HATEOAS API—the "Invisible UI" for Artificial Intelligence. This API doesn't just serve data; it serves meaning. It tells the AI exactly what an object is, how it relates to others, and—crucially—what actions are legally allowed at this exact second.
Infrastructure Zero
The beauty of this approach is simplicity. You do not need a Vector Database cluster, a GPU farm, or an expensive SaaS integration platform.
The deployed web app is the only infrastructure you need.
Once you hit "Publish," your app functions as a Micro-Ontology Host. It is instantly live.
- Humans log in via the browser to verify data and approve tasks.
- Digital Co-Workers connect via the API to analyze data and execute workflows.
They share the same brain, the same rules, and the same database connection.
From Micro to Macro: The Federated Mesh
The biggest mistake companies make is trying to build a "Monolith"—one giant brain that knows everything. This creates security risks and hallucination loops (e.g., confusing "Sales Leads" with "Lead Poisoning").
We propose a Gradual Architecture.
- Start Small: Build a "Sales App." It is the Micro-Ontology for customers and deals.
- Expand: Build an "Inventory App." It is the Micro-Ontology for products and stock.
- Federate: Use our built-in Federated Identity Management (FIM) to link them.
With FIM, a Digital Co-Worker in the Sales App can seamlessly "hop" to the Inventory App to check stock levels. It carries the User's Identity across the boundary, ensuring it only sees what that specific user is allowed to see. You build a Unified Enterprise Ontology not by centralizing data, but by connecting it.
Safety and Cost Control
This architecture solves the two biggest fears of the CIO: Runaway Costs and Data Gravity.
- Identity-Based Constraints: The AI runs as the user. It is limited by the user's role, ensuring it cannot access sensitive HR files or approve unbudgeted expenses.
- Cost Containment: You control the loop. You define how many iterations the Co-Worker can run, how much time it can spend, and which LLM flavor (GPT-4o, Gemini, Claude) it uses.
- Zero Data Gravity: With our BYOK (Bring Your Own Key) model, you pay your AI provider directly for consumption. Your data stays in your database. It is never trained into a public model, and you are never locked into our platform.
Stop Training. Start Building.
You don't need expensive consultants to interpret your data. You know your business.
Use App Studio to define it. Use the Micro-Ontology Factory to deploy it. And let your Digital Workforce run it.
Learn more about the Micro-Ontology Factory.