Micro Ontology

Why the fastest way to teach AI your business is to build an app.

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Micro Ontology
Monday, December 1, 2025PrintSubscribe
Building the Enterprise Ontology for AI

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:

  1. Extracting petabytes of data into a centralized Data Lake.
  2. Training or Fine-Tuning a custom model (at massive GPU cost).
  3. Building Robots (RPA) to click buttons on your legacy screens.
  4. 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.

  1. Start Small: Build a "Sales App." It is the Micro-Ontology for customers and deals.
  2. Expand: Build an "Inventory App." It is the Micro-Ontology for products and stock.
  3. 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.
Monday, December 1, 2025PrintSubscribe
The Living Ontology: Refactor Intelligence at the Speed of Thought

For decades, the "Enterprise Ontology"—the definitive map of your business data and rules—has been treated as a static artifact. It’s usually a massive documentation project or a rigid Data Warehouse schema that takes months to build and years to update. By the time it’s finished, the business has already moved on.

At Code On Time, we believe the Ontology shouldn’t be a document. It should be Software.

With the Micro-Ontology Factory, we are introducing a radical shift: The App IS the Ontology.

From Static Schema to Living Application

When you build a database web app with App Studio, you aren't just building screens for humans. You are simultaneously generating a self-describing HATEOAS API that serves as the cognitive map for your AI Agents.

This tightly coupled architecture unlocks capabilities that static data lakes can never match:

  • Infinite Refactoring: Need to track a new metric or enforce a new business rule? Don't file a ticket with the Data Team. Just update the app in App Studio. Your AI Co-Workers instantly "see" the new logic and adapt their behavior.
  • Unified Operations & Analytics: Stop separating "doing work" (OLTP) from "measuring work" (OLAP). Build analytical dashboards directly into the visible UI to give your Agents explicit "thinking paths" for complex questions.
  • Disposable Intelligence: Because Micro-Ontologies are fast and cheap to generate, you can spin up a temporary, single-purpose "Headless App" to solve a specific problem (like a merger or audit) and discard it when the job is done.
  • Real-Time Governance: Control the economics of your digital workforce. Instantly switch user roles from expensive "Reasoning" models to fast "Flash" models, or cap daily spending limits across the enterprise—all without a deployment cycle.

The Future is Agile

Intelligence is no longer a capital expenditure; it is a flexible operational expense. It’s time to stop treating your data model like a museum piece and start treating it like a living, breathing business asset.

Ready to see how it works?

Read the full vision: The Agile Ontology
Monday, November 24, 2025PrintSubscribe
The "Brain in a Jar" Paradox

We are living through an "Intelligence Boom."

The latest Large Language Models (LLMs) can pass the Bar Exam, write Shakespearean sonnets about your quarterly earnings, and debug complex Python scripts in seconds. They are, by all accounts, geniuses.

But there is a problem.

If you ask that genius AI to perform a simple, mundane task—like "Update this customer's phone number"—it hits a wall.

It might say: "I cannot access your database directly."

Or worse, if you’ve rigged up a custom connection, it might say: "I’ve updated the number," while secretly hallucinating a format that breaks your downstream SMS provider.

Potential vs. Kinetic Energy

The current generation of Enterprise AI is stuck in a state of Potential Energy.

It has the potential to reason about your business, but it lacks the Kinetic Energy to actually move it forward.

It is a Brain in a Jar.

It sits on a shelf (or in a chat window), disconnected from the physical reality of your business data. It can observe, analyze, and comment, but it cannot touch.

The "90/10" Reality of Business

This is a critical failure because business is not 90% "Thinking." Business is 90% Doing.

  • 10% Inference (The Brain): "Analyze these sales trends." "Draft a polite email." "Summarize this meeting."
  • 90% Operations (The Hands): "Post this invoice." "Update that inventory count." "Schedule the installation." "Flag this account for review."

Most AI technology providers are selling you engines that excel at the 10% but are paralyzed at the 90%. They offer you a "Copilot" that can explain the flight manual in perfect detail but cannot actually reach the control stick.

The Trap of "Building Hands" (MCP)

To solve this, the industry has rallied around concepts like the Model Context Protocol (MCP) or "Function Calling." The idea is simple: You write code to give the AI a "Hand."

  • You write a function: update_phone_number(id, number).
  • You teach the AI how to use it.
  • You pray the AI uses it correctly.

The problem? You have to build a new hand for every single action in your enterprise. If you have 1,000 database tables, you are looking at building 5,000+ custom tools. And once you build them, you have to write "Safety Manuals" (System Prompts) to ensure the AI doesn't accidentally delete the wrong record.

It is expensive, risky, and fragile. It turns your development team into "Prosthetics Engineers."

The Solution: Give the Brain a Body

At Code On Time, we believe you shouldn't have to build hands from scratch. You already have them.

Your existing business applications—the forms, the grids, the validation rules, the security roles—are the Hands. They already know how to safely update a phone number. They already know that "Inventory cannot be negative."

Our Micro-Ontology technology (powered by the built-in Axiom Engine) simply connects the "Brain" (Your LLM of choice) to the "Body" (Your Application).

  • The Brain provides the intent: "Update the phone number to 555-0199."
  • The Body (Code On Time) executes the action using the HATEOAS API.

It doesn't hallucinate the update logic because it doesn't invent the update logic. It uses the exact same logic your human employees use every day.

The Right Brain for the Job

Because the "Body" handles the safety and execution, you are free to swap the "Brain" based on the user's role.

  • For the CEO (Strategy): Give their Digital Co-Worker a high-end Reasoning Model (like GPT-4o or Claude 3.5 Sonnet) and Read-Only Access to all customer orders.
    • The Prompt: "Write a data poem analyzing our Q4 churn rate vs. competitor pricing."
    • The Result: Deep, strategic insight. Expensive compute, but worth it for the 10% of strategic decisions.
  • For the Employees (Operations): Give their Digital Co-Worker a fast, efficient Flash Model (like Gemini 1.5 Flash).
    • The Prompt: "Reschedule the Jones appointment to Tuesday."
    • The Result: Instant, error-free execution. Low cost ($0.0004/task), perfect for the 90% of daily grind. Performed via SMS.

You don't have to choose between "Smart" and "Safe." You can have the Genius in the boardroom and the Diligent Worker in the mailroom, both running on the same secure platform.

From "Chatbot" to "Co-Worker"

When you connect a Brain to a Body, you stop getting a "Chatbot" and start getting a Digital Co-Worker.

  • A Chatbot writes a poem about your data.
  • A Co-Worker fixes your data.
  • A Chatbot suggests you email the client.
  • A Co-Worker sends the email (after you approve the draft).

Don't settle for a genius on a shelf. Give your AI the hands it needs to get to work.

Ready to unleash Kinetic AI?
Discover how the Digital Co-Worker moves your business
Labels: AI, Micro Ontology