OntoGraph

Codename: The Ambiguity Firewall Version: 1.0 Status: Draft Strategic Alignment: Phase 1 (Foundation/Structure) of the Machine-First Maturity Model.

1. Executive Summary

The Core Problem: Modern AI systems do not fail because they lack intelligence; they fail because the world they are asked to reason about is poorly structured. When definitions are loose, models interpolate meaning, leading to "silent failures" and hallucinations. The Solution: OntoGraph is a Taxonomy Governance Engine and Ambiguity Firewall. It replaces free-text descriptors with "controlled vocabularies," forcing organizations to explicitly define "what exists," "how things relate," and "what cannot happen". The Goal: To move client data from UX (optimized for human interpretation) to RX (Reasoning Experience), ensuring machines can reason about the business without guessing.

2. User Personas

• The Knowledge Architect (Primary): Responsible for defining the "truth" of the product catalog or policy set.

• The AI Product Owner: Needs to prevent the LLM from hallucinating capabilities that do not exist.

• The Compliance Officer: Needs to ensure that specific terms (e.g., "Sulfate-Free" vs. "Clean") are used consistently across all machine interfaces.

3. Core Value Proposition

• Kill Ambiguity: Prevents teams from using the same word to mean different things, which causes silent failures in AI reasoning.

• Ontology Before Intelligence: Provides the structure necessary for a small model to outperform a large model by replacing inference with lookup.

• Drift Prevention: Monitors the semantic decay of terms over time.

4. Functional Requirements

4.1. The Controlled Vocabulary Builder

• Requirement: The system must allow users to define a finite, limited set of entities and attributes.

• Rationale: Taxonomies must be intentionally limited to constrain meaning. Unlimited tags create entropy.

• Key Feature: "Mutually Exclusive" Enforcement. The system must flag when two categories overlap ambiguously (e.g., preventing an item from being tagged both "Fragrance-Free" and "Scented").

4.2. The "Ambiguity Firewall" (Input Guardrails)

• Requirement: An ingestion gate that rejects unstructured or ambiguous input from the CMS or PIM (Product Information Management) systems.

• Rationale: "Garbage in, Hallucination out." If "Clean Beauty" is passed to the AI without a definition, the AI will invent one.

• Key Feature: The "Other" Monitor. The system must track usage of the "Other" category. A spike in "Other" indicates the taxonomy is failing to capture reality and requires immediate human intervention.

4.3. The "Do Not Infer" Registry

• Requirement: A dedicated module to define negative constraints—what does not exist or what the AI is not allowed to infer.

• Rationale: An ontology must define "what cannot happen" to prevent the model from bridging gaps logically but incorrectly.

• Key Feature: Explicit "Do Not Infer" lists. (e.g., "Do not infer 'organic' solely from 'natural'").

4.4. Knowledge Version Control

• Requirement: Every definition and relationship must be versioned (e.g., Taxonomy v2.1).

• Rationale: Knowledge changes. Rules evolve. Systems that treat knowledge as static documents degrade silently. The AI must know which version of reality applied at the time of a decision.

4.5. Reasoning Experience (RX) API

• Requirement: Output must be structured for machine comprehension, not human display.

• Rationale: UX is about simplification; RX is about explicit structure.

• Key Feature: The API returns deterministic relationships. Instead of a text blob, it returns: {"subject": "Product X", "attribute": "Vegan", "status": "Verified", "relationship": "Is_A"}.

5. Non-Functional Requirements

• Legibility: The schema must be readable by both humans (for governance) and machines (for reasoning).

• Interoperability: The ontology must "travel well" across ecosystems (e.g., distinct from internal naming conventions, aligned with schema.org or industry standards where applicable).

• Latency: Taxonomy lookups must occur in <50ms to serve as a pre-processing step for real-time AI agents.

6. Use Cases

Use Case A: The "Clean Beauty" Trap (Beauty Industry)

• Problem: A brand uses "Clean" to mean "Paraben-Free." An AI agent infers "Clean" means "Organic." The agent recommends the product to a user seeking organic goods. The user sues or returns the item.

• OntoGraph Solution: The system rejects the tag "Clean" at the ingestion layer. It forces the brand to map "Clean" to the specific attribute contains_parabens: false. The AI receives the explicit chemical fact, not the marketing vibe.

Use Case B: The "Safe Action" Check (FinTech)

• Problem: An AI agent attempts to execute a trade based on a "low risk" categorization that has drifted over time.

• OntoGraph Solution: The system checks the Risk_Definition_v4. It sees the asset class has moved to "medium risk" in the latest version. It triggers a "Refusal" rule because the definition has changed, preventing the action.

7. Success Metrics (KPIs)

We will not measure "Engagement" or "Time on Site." We will measure Structure & Judgment:

1. Ambiguity Rate: The percentage of queries or products falling into "Other" or undefined categories (Target: <5%).

2. Inference Reduction: A measurement of how often the AI model has to "guess" a relationship vs. looking it up in the OntoGraph (Target: 90% lookup / 10% inference).

3. Hallucination Prevention: A reduction in "silent failures" where the model confidently gives the wrong answer due to loose definitions.

8. Roadmap

• Phase 1 (MVP): Manual Taxonomy Builder & "Other" Monitoring.

• Phase 2: API Integration for "Just-in-Time" definitions for AI Agents.

• Phase 3: Automated "Drift Detection" using outcomes to flag outdated definitions.