AIF is the foundational reasoning architecture behind every AIR-powered application. It separates domain knowledge from intelligence, so the same framework operates across industries while adapting to different data models and business contexts.
Framework layers
8
Objectives
8
Core principles
7
Evidence loaded
6
Framework
The 8 layers of AIF
01
Perception Layer
Ingest and interpret real-world signals across every AIREx surface
02
Knowledge Layer
Organize domain facts, entities, ontologies, and precedents
03
Reasoning Layer
Chain evidence into inferences with explicit steps
04
Prediction Layer
Forecast outcomes and quantify uncertainty over defined horizons
05
Strategy Layer
Compose alternatives, weigh trade-offs, and recommend action pathways
06
Execution Layer
Draft the artifacts and workflows humans approve before anything is done
07
Learning Layer
Feed decision outcomes back into models, evidence, and heuristics
08
Governance Layer
Enforce evidence-before-inference, org isolation, and audit trails
Objectives
What AIF does
Observe
Detect meaningful signals across sources
Understand
Ground signals in domain knowledge
Reason
Chain evidence into defensible inferences
Predict
Forecast outcomes with quantified uncertainty
Recommend
Propose ranked, explainable actions
Simulate
Model counterfactuals and future scenarios
Learn
Improve continuously from outcomes
Explain
Show the reasoning behind every conclusion
Core principles
How AIF works
Evidence before inference
No claim ships without cited, retrievable evidence.
Explainability by default
Every output exposes its reasoning path, not just the answer.
Human accountability
AI proposes; identified humans decide, and decisions are logged.
Organization isolation
Each org's knowledge, models, and audit trail are hard-partitioned.
Composable intelligence
Layers plug into any AIR-powered app across industries.
Continuous learning
Outcomes update models, evidence, and confidence over time.
Deterministic auditability
Every run is reproducible: same inputs → same reasoning trace.
Structured output
Every AIF run returns
Evidence
Confidence
Assumptions
Alternatives
Trade-offs
Recommended actions
AIF output is informational. Recommendations flagged approval-required must be reviewed by an accountable human before AIREx or any AIR-powered application acts on them.