AI architecture
AI-ready data is an architecture problem
LeanSignal technical brief · Enterprise data & systems architecture.
An AI platform can be deployed quickly. Creating reliable enterprise context for that platform is usually slower because the underlying data remains fragmented across systems, semantics and governance models.
AI needs more than access.
Models and agents need data that is discoverable, contextual, traceable, sufficiently current and governed for the intended use.
The architecture requirements
- Semantic consistency across source systems
- Traceable provenance and quality context
- Metadata, lineage and provenance
- Policy-aware machine access
- Explicit model, data and tool dependencies
- Controlled feedback and write-back paths
The fastest way to create an AI silo is to build a separate data architecture exclusively for AI.
Architecture principle
Expose trusted enterprise context through governed contracts rather than wiring models directly into each source system.
General architecture guidance. Specific environments require adaptation to their security, regulatory, operational and technology constraints.