Architecture
Architecture for Enterprise AI That Must Be Trusted
Business logic remains deterministic where it should. Semantic context is prepared before model interpretation. The model is one component in a governed system, not the system itself.
The Orisdale reference architecture
One engineered system, not seven unrelated boxes: enterprise data enters at the top, the Orisdale intelligence core governs AI interpretation in the middle, and monitoring with human review wraps the entire system.
Enterprise business inputs
Trusted business outcomes
Why model-first architecture fails
Connecting a model directly to raw enterprise data, without governed logic or semantic preparation in between, produces answers that look confident and are not reproducible. Two people ask the same question and get two different numbers, because the model recalculated a business definition on the fly instead of reading one that was already governed.
Layer by layer
Select a layer to understand its role, responsibilities, and controls within the complete system. Every summary is always visible; select a layer for expanded technical detail. Every detail is reachable by keyboard and on mobile — nothing important is hidden behind hover. For more on how the Agentic AI Runtime layer is constrained, see Enterprise AI Agents, Governed by Design.
CRM, ERP, financial systems, and other enterprise sources of record.
- Data remains in place inside systems the organization already operates and governs.
- Orisdale does not require a separate copy of the enterprise's source-of-truth systems.
Calculations, thresholds, classifications, and KPI definitions, controlled before AI interpretation.
Business context, relationships, and operating rules prepared for AI consumption.
Snowflake, Databricks, Google Cloud, and Alteryx One provide governance and compute.
AI experiences constrained by approved tools, data, and policies.
The interface where business users and executives interact with governed intelligence.
Control boundary
Monitoring, Control, and Human Review
Quality, access, cost, and behavior monitoring, with human approval in consequential workflows.
Security and governance
Deterministic logic, AI interpretation, and human review form one connected governance boundary around every Orisdale system.
Deterministic logic
Calculations, thresholds, and classifications are defined, reviewed, and controlled before any AI interpretation.
AI interpretation
The AI runtime explains and contextualizes governed outputs within approved tools and permissions.
Human review
Consequential decisions retain human approval, with monitoring for quality, access, cost, and behavior.
Deployment patterns
The same reference architecture deploys on Snowflake, Databricks, Google Cloud, or a combination, depending on where an organization’s governed data and AI runtime already live. See how each platform fits on the Platforms page.
Single-platform intelligence system
Governed logic, semantic intelligence, and the AI runtime all run inside one enterprise platform's native governance boundary.
Governed preparation plus enterprise data platform
A governed preparation layer standardizes business logic before publishing AI-ready data into the enterprise data platform.
Cross-platform governed intelligence system
Shared governed logic and semantic definitions reconcile data across more than one enterprise platform before AI interpretation.
Model-agnostic by design
Governed business logic and semantic intelligence remain stable while the approved model or platform-native AI service can change according to the customer's architecture and governance requirements.
These are technical runtime options — not universal dependencies, not all used in every implementation, and not partnership claims. The runtime remains controlled by the approved customer architecture.
Technical runtime options
- Claude
- OpenAI
- Gemini
- Platform-native AI
Guardrails
- Approved models only
- Governed context and tools
- Monitored outputs
- Business logic remains deterministic where it should.
- Semantic context is prepared before model interpretation.
- Data remains governed by the enterprise platform.
- Model access is constrained by approved tools and permissions.
- Outputs identify source, scope, and limitations where appropriate.
- Human approval remains in consequential workflows.
- Monitoring covers quality, access, cost, and behavior.