AICE
Assistive & Intelligence Engines
1. Purpose
Assistive & Intelligence Engines (AICE) provide probabilistic, interpretive, and diagnostic capabilities that support — but do not replace — deterministic computation within ZAYAZ.
AICE engines enhance understanding, interpretation, and decision support by applying AI-driven reasoning, language understanding, and diagnostic analysis to ESG-related data and workflows.
AICE is a distinct architectural layer, separate from the deterministic Micro Engines (MICE) and orchestrated through ZARA.
2. Position in the Architecture
ZAYAZ distinguishes between three complementary layers:
MICE → AICE → ZARA
-
MICE
Deterministic, replayable, and auditable signal processing. -
AICE
Probabilistic, assistive, and diagnostic intelligence. -
ZARA
Orchestration, explainability, governance, and human-facing interaction.
AICE engines may consume outputs from MICE pipelines and provide advisory insights to ZARA-controlled workflows.
3. Determinism vs Intelligence
AICE engines differ fundamentally from MICE engines:
| Aspect | MICE | AICE |
|---|---|---|
| Determinism | Required | Not guaranteed |
| Replayability | Guaranteed | Context-dependent |
| Output role | Authoritative | Advisory by default |
| AI / LLM usage | Not required | Common |
| Audit semantics | Structural & numeric | Interpretive & probabilistic |
AICE outputs must never be treated as authoritative signals unless explicitly approved and governed.
4. Scope of Responsibility
What AICE Engines Do
- Interpret unstructured or semi-structured content
- Classify or score information using AI models
- Corroborate or challenge facts using external sources
- Diagnose anomalies and propose likely root causes
- Generate explanations, hypotheses, or recommendations
AICE engines are designed to assist humans and systems, not to replace deterministic logic.
5. What AICE Engines Do Not Do
- ❌ Emit canonical USO signals by default
- ❌ Replace deterministic computation (MICE)
- ❌ Enforce policy decisions or compliance gates
- ❌ Modify authoritative data values directly
All AICE outputs are subject to governance, confidence handling, and human oversight.
6. Canonical AICE Engine Categories
The following engine categories form the AICE layer.
6.1. NLPI — NLP Interpretation Engines
Interpret user input or documents using NLP and LLM-based models to produce classifications, scores, or structured interpretations.
Typical uses:
- Document classification
- Narrative interpretation
- Text-to-structure mapping
6.2. AIFA — AI-Fact Assist Modules
Assist in confirming or challenging facts using external AI models, knowledge sources, or reasoning systems.
Typical uses:
- Fact corroboration
- Cross-source consistency checks
- Confidence-weighted confirmations
6.3. RCAS — Root Cause Analysis Engines
Analyze signals and execution outcomes to diagnose anomalies and propose likely root causes or corrective actions.
Typical uses:
- Field-level issue diagnosis
- Data quality investigation
- Explainable anomaly analysis
7. Governance & Trust Model
All AICE engines operate under explicit governance:
- Outputs are confidence-scored
- Provenance and model context are recorded
- Results are clearly labeled as:
- advisory
- provisional
- or informational
ZARA controls:
- when AICE engines are invoked,
- how their outputs are presented,
- whether human review is required.
8. Relationship to CFIL and Policy Gates
AICE outputs may be passed through:
- CFIL — Confidence Filter Engines
- Policy gates (TRPG / ZADIF)
before influencing downstream workflows.
This ensures that probabilistic intelligence never silently alters authoritative data.
9. Design Rationale
Separating AICE from MICE ensures that:
- deterministic computation remains auditable,
- intelligence remains transparent and governable,
- AI-assisted insights enhance trust rather than erode it.
AICE makes intelligence explicit, not implicit.
10. Next: AICE Engine Deep Dives
The following chapters describe individual AICE engine categories in detail:
- NLPI — NLP Interpretation Engines
- AIFA — AI-Fact Assist Modules
- RCAS — Root Cause Analysis Engines
Each chapter defines scope, limitations, and governance expectations.
Status: Stable
Layer: Intelligence Hub
Owner: ZARA / Intelligence Governance