Getting Started
Conceptual overview of Reality Fidelity integration
Integrating Reality Fidelity requires domain expertise, architectural decisions, and ongoing calibration. This overview describes the integration pattern — the specific implementation depends on your domain, infrastructure, and risk profile.
The Integration Pattern
Reality Fidelity sits as a governance layer between your AI system and authoritative outputs. The pattern is consistent; the implementation is domain-specific.
Proposals pass through. Measurements require completeness and provenance.
Integration Phases
Identify High-Risk Entities
Map the outputs in your domain where missing context causes harm. These are your governance targets — the entities that require completeness validation before authority is granted.
- Which outputs carry liability if wrong?
- Where does confident fiction cause real damage?
- What assumptions does your AI currently make silently?
Define Required State
For each high-risk entity, determine the minimum state that must be explicitly specified. This becomes your completeness gate — the boundary between proposals and authoritative outputs.
- What parameters change the correct answer?
- Which missing inputs lead to incorrect outputs?
- How granular should specification requirements be?
Design Authority Hierarchy
Establish provenance sources and override rules. Define what counts as an oracle in your domain and how human locks interact with automated validation.
- What external sources back authoritative claims?
- When can automated systems override each other?
- How do human decisions propagate through the system?
Implement Governance Layer
Wire the completeness gate and provenance validators into your existing AI pipeline. The governance layer sits as middleware — intercepting requests and gating authoritative outputs.
- Where in the request flow does validation occur?
- How do specification requests surface to users?
- What telemetry captures governance decisions?
Tune and Expand
Monitor gate activations and refine entity schemas based on real usage. Expand coverage from initial high-risk entities to broader domain governance.
- Which specification requests indicate schema gaps?
- Where is the gate too strict or too permissive?
- What new entity types need governance?
Why This Is Hard
The pattern is simple. The execution requires expertise.
Defining required state requires deep domain expertise. Over-specification creates friction; under-specification permits errors.
Connecting to authoritative sources — regulatory databases, clinical protocols, standards registries — requires integration work.
Real-world inputs rarely match clean schemas. Handling partial specification and conflicting sources requires careful decisions.
Governance checks add latency. At scale, efficient validation, caching strategies, and async patterns become critical.
What Ontic Labs Provides
We've solved the hard problems so you can focus on your domain.
- Domain Schema Libraries — Pre-built entity schemas for healthcare, finance, legal, insurance, government, and engineering domains
- Provenance Connectors — Integrations with authoritative sources and regulatory databases
- Governance SDK — Drop-in middleware for completeness validation and authority gating
- Implementation Support — Domain expertise for schema design and calibration
Start the Conversation
Reality Fidelity implementation starts with understanding your domain's risk profile and existing AI architecture. We'll help you identify high-risk entities, design appropriate governance, and integrate without disrupting your existing systems.
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