01 / Overview
Property inputs become structured intelligence.
Convertis is built around a clear decision artifact, not a chat response: score, rationale, saved record, and reviewable output.
02 / Architecture
A bounded workflow from intake to persistence.
The architecture separates validation, enrichment, scoring, AI interpretation, and delivery so each step can be explained and improved.
Input & Validation
Address, property type, and required fields are normalized before analysis.
Location Intelligence
Nearby amenities, transit, urban context, and geospatial signals enrich the property record.
Persistence
Records are stored for retrieval, comparison, and review instead of disappearing after generation.
03 / Production Logic
AI supports interpretation; scoring stays explainable.
The production flow keeps deterministic scoring separate from AI-generated interpretation, which improves trust and makes outputs easier to audit.
Deterministic Scoring
Weighted criteria produce a transparent score rather than an opaque model judgment.
AI Boundary
The model helps summarize and interpret structured context; it is not the hidden authority behind the score.
Reviewability
Scorecards, logs, and saved outputs support human inspection and operational reuse.
04 / Outputs
The output is a decision artifact.
Convertis packages analysis into formats a business user can inspect, compare, and revisit.
05 / Engineering Signals
Key AI engineering decisions are visible.
The project demonstrates production-minded AI engineering patterns without exposing private implementation detail.
Typed Input → Structured Output
The workflow preserves data contracts from intake through scorecard delivery.
Graceful AI Use
AI improves readability while deterministic fallbacks keep the workflow resilient.
Production Discipline
Server-only secrets, validation, logging, and deployment checks are part of the system design.
Business Fit
The system focuses on clarity, confidence, and repeatable property review.
Technical Appendix
Key code patterns, shown without private implementation detail.
These excerpts show the core AI engineering ideas behind the workflow: validation, source traceability, and structured outputs.
Input Contract
Bounded property intake
The workflow starts with typed, normalized inputs so downstream scoring and AI interpretation work from a stable contract.
type PropertyScreeningInput = {
address: string;
propertyType: "office" | "retail" | "industrial" | "mixed_use";
buildingSizeSf?: number;
market?: string;
};
const inputRules = {
required: ["address", "propertyType"],
normalize: ["address", "market"],
confidenceFields: ["geocodeConfidence", "sourceConfidence"],
};Scoring Logic
Explainable weighted score
A deterministic scoring layer keeps the score reviewable. AI can explain the result, but the numeric outcome comes from visible criteria.
const weights = {
location: 0.3,
demand: 0.25,
buildingQuality: 0.2,
marketDynamics: 0.15,
riskProfile: 0.1,
};
function calculateScore(criteria: Record<keyof typeof weights, number>) {
return Object.entries(weights).reduce((total, [key, weight]) => {
return total + criteria[key as keyof typeof weights] * weight;
}, 0);
}AI Boundary
Structured interpretation request
The AI layer receives structured context and returns a bounded response, preserving the difference between analysis data and narrative synthesis.
{
"task": "summarize_scorecard",
"inputs": {
"score": 86,
"criteria": ["location", "demand", "riskProfile"],
"locationSignals": ["walkable", "transit_access", "high_growth"],
"approvedSourcesOnly": true
},
"outputContract": {
"summary": "string",
"rationale": "string[]",
"risks": "string[]",
"confidence": "number"
}
}Example Structured Output
{
"project": "Archangel – Convertis",
"workflow": "property_screening",
"input": {
"property_type": "mixed_use_retail",
"market": "South Florida",
"address_status": "geocoded"
},
"signals": {
"amenity_count": 42,
"retail_concentration": "high",
"transit_proximity": "medium",
"data_confidence": 0.82
},
"scorecard": {
"overall_score": 78,
"grade": "B+",
"criteria": [
{
"name": "commercial_density",
"score": 84,
"explanation": "Nearby-place concentration supports customer access."
},
{
"name": "accessibility",
"score": 71,
"explanation": "Transit proximity is acceptable but not dominant."
}
]
},
"review": {
"analyst_review_required": true,
"approved_sources": 5,
"benchmark_ready": true
}
}