4. Solution Strategy
Technology Decisions
Agentic AI vs Generative AI
Decision: Use agentic AI (Smitty) for market sentiment analysis
| Aspect | Agentic AI (Chosen) | Generative AI |
|---|---|---|
| Purpose | Takes autonomous actions to accomplish goals | Creates content (text, images) |
| Behavior | Goal-directed, learns from outcomes | Prompt-based, stateless generation |
| CRE Application | Analyzes 6 sentiment layers, calculates weighted scores, learns from results | Could generate appraisal narratives but not perform analysis |
| Learning | Continuous via memory bank feedback loop | No built-in learning from outcomes |
| Differentiation | Technical moat, defensible approach | Easily replicated with ChatGPT-style tools |
Rationale:
- CREstimate.ai needs goal-directed behavior (accurate sentiment scoring), not text generation
- Agentic AI enables self-correction: each valuation outcome improves future predictions
- Provides competitive differentiation in a market where generative AI is becoming commoditized
See: ADR-001: Agentic AI Approach for full decision record
CRESI 6-Layer Architecture
CRESI (Commercial Real Estate Sentiment Index) analyzes market sentiment through six independent data layers:
The Six Layers
- Macro-Economic: GDP, inflation, interest rates, federal policy
- Micro-Economic: Local vacancy rates, rent growth, employment, MSA-specific indicators
- Geopolitical: Policy changes, regulations, trade agreements, political stability
- Capital Markets: Cap rates, transaction volume, debt availability, investor appetite
- Demographics: Migration patterns, population growth, employment shifts, age demographics
- Current Events: Recent market developments, sentiment signals, breaking news impact
Asset-Specific Weighting: Each property type (office, retail, industrial, multifamily) has different layer weights based on historical correlations.
See: ADR-002: CRESI Architecture for decision rationale
Top-Level Decomposition
System Components
Key Components:
- Smitty AI Agent: Agentic AI economist that orchestrates sentiment analysis
- CRESI Calculator: Aggregates 6 sentiment layers with asset-specific weights
- Memory Bank: Stores historical valuations and outcomes for continuous learning
- Valuation Pipeline: Integrates CRESI scores with traditional appraisal methods
Achieving Quality Goals
| Quality Goal | Architectural Approach |
|---|---|
| Architectural Clarity | Arc42 template with progressive disclosure (overview → details), diagrams for visual understanding |
| Version Transparency | Git-based semantic versioning + automated changelog from conventional commits |
| Accessibility | WCAG 2.1 AA compliance, alt text for diagrams, keyboard navigation, screen reader support |
| Living Documentation | Docusaurus for fast builds, section-level last-modified tracking, "What's New" highlights |
Key Architectural Patterns
Agentic Learning Loop
The core pattern differentiating CREstimate.ai from traditional systems:
- Input: Property data + 6 sentiment layers
- Process: Smitty calculates weighted sentiment score
- Output: Valuation adjustment applied to base appraisal
- Feedback: Actual market outcome stored in Memory Bank
- Learn: Smitty adjusts future weighting based on accuracy
This feedback loop enables continuous improvement without human intervention.
Sentiment Layer Independence
Each of the 6 CRESI layers operates independently:
- Benefit: Failure of one layer doesn't break the system
- Scalability: New layers can be added (e.g., ESG factors, climate risk)
- Explainability: Each layer's contribution is traceable for regulatory review
Integration with Traditional Appraisals
CREstimate.ai augments (not replaces) traditional valuation methods:
Traditional Methods (Base Valuation):
- Income Approach
- Sales Comparison Approach
- Cost Approach
- Comparable Properties Analysis
+ CRESI Adjustment (Market Sentiment):
- Smitty's 6-layer sentiment score
- Asset-specific weighting
- Historical outcome learning
= CREstimate.ai Value: More accurate, consistent, and defensible
For implementation details of the agentic AI approach, see Building Block View and Runtime View.