🏗️ SITESYNAPSE

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⚡ AI PROPERTY INSPECTION COPILOT

Vision-Grounded Measurements • Citation-Backed Compliance • Multi-Agent Escalation

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🌍 The Problem

Traditional property and infrastructure inspections often rely on:

❌ Manual observations

❌ Subjective assessments

❌ Missing quantitative measurements

❌ No automated compliance validation

❌ Inconsistent documentation

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🚀 The Solution

SiteSynapse combines:

🧠 Computer Vision

📚 Retrieval-Augmented Generation (RAG)

⚖ Rule-Based Compliance Evaluation

🤖 Multi-Agent Orchestration

📊 LLMOps Evaluation

👨‍⚖ Human-in-the-Loop Escalation

to transform raw inspection imagery into:

✔ Real-world measurements

✔ Structural defect analysis

✔ Code-compliance validation

✔ Citation-grounded decisions

✔ Audit-ready inspection reports

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⚡ CORE CAPABILITIES

👁 Vision AI Depth Estimation Defect Detection Spatial Measurements Dimension Extraction

📚 RAG Engine TF-IDF Retrieval Keyword Expansion Code Lookup Citation Grounding

🤖 Agents VisionAgent ComplianceAgent AdjudicatorAgent ReportAgent

📊 LLMOps Run Tracing Config Registry Regression Tests Evaluation Harness

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🧠 MULTI-AGENT INSPECTION PIPELINE

╔══════════════════════════════════════════════════════════════════════╗
║                         SITE SYNAPSE                               ║
║             AI PROPERTY INSPECTION COPILOT                         ║
╚══════════════════════════════════════════════════════════════════════╝

                     Inspection Image
                             │
                             ▼

┌──────────────────────────────────────────┐
│               VisionAgent                │
├──────────────────────────────────────────┤
│ • Defect Detection                       │
│ • Depth Estimation                       │
│ • Real-world Measurements                │
└──────────────────────────────────────────┘
                             │
                             ▼

┌──────────────────────────────────────────┐
│             ComplianceAgent              │
├──────────────────────────────────────────┤
│ • Hybrid Retrieval                       │
│ • TF-IDF Search                          │
│ • Keyword Expansion                      │
│ • OSHA / IBC / IRC Lookup                │
└──────────────────────────────────────────┘
                             │
                             ▼

┌──────────────────────────────────────────┐
│            RuleBasedJudge                │
├──────────────────────────────────────────┤
│ PASS                                     │
│ FAIL                                     │
│ AMBIGUOUS                                │
└──────────────────────────────────────────┘
                             │
                             ▼

┌──────────────────────────────────────────┐
│            AdjudicatorAgent              │
├──────────────────────────────────────────┤
│ Confidence Thresholding                  │
│ Human Escalation Logic                   │
│ Review Notes                             │
└──────────────────────────────────────────┘
                             │
                             ▼

┌──────────────────────────────────────────┐
│              ReportAgent                 │
├──────────────────────────────────────────┤
│ Markdown Report Generation               │
│ Compliance Citations                     │
│ Measurement Summary                      │
└──────────────────────────────────────────┘
                             │
                             ▼

┌──────────────────────────────────────────┐
│                RunLogger                 │
├──────────────────────────────────────────┤
│ JSONL Traces                             │
│ Latency Metrics                          │
│ Escalation Statistics                    │
│ Evaluation Analytics                     │
└──────────────────────────────────────────┘

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🔬 AI AGENTS

👁 VisionAgent

Perception layer responsible for extracting quantitative observations.

Features

Depth Estimation:
  Monocular Depth Processing

Defect Detection:
  Crack Detection
  Surface Damage
  Structural Anomalies

Measurements:
  Length Estimation
  Depth Estimation
  Physical Extent Analysis

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📚 ComplianceAgent

Retrieves building regulations and safety standards.

Supported Sources

IRC:
  International Residential Code

IBC:
  International Building Code

OSHA:
  OSHA 1926 Safety Standards

Hybrid Retrieval Engine

Retrieval:
  TF-IDF Lexical Search

Expansion:
  Semantic Keyword Expansion

Ranking:
  Top-K Document Selection

Output:
  Citation Grounded Evidence

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⚖ RuleBasedJudge

Determines compliance verdicts.

PASS:
  Meets Standards

FAIL:
  Violates Standards

AMBIGUOUS:
  Requires Additional Review

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👨‍⚖ AdjudicatorAgent

Human-in-the-loop escalation framework.

Confidence Score < 0.40:
  Escalate

Borderline Findings:
  Escalate

Conflicting Evidence:
  Escalate

Output:
  Review Notes
  Escalation Summary

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📝 ReportAgent

Generates inspection-ready documentation.

Generated Report Includes

Inspection Summary
Defect Findings
Physical Measurements
Compliance Verdicts
Rule Citations
Escalation Notes
Final Recommendation

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📊 LLMOPS OBSERVABILITY

Configuration Registry

v1_mock_baseline:
  Baseline Inspection Logic

v2_stricter_escalation:
  Increased Compliance Sensitivity

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Run Tracing

Every inspection produces structured telemetry.

{
  "inspection_id": "run_184",
  "latency_ms": 284,
  "findings": 3,
  "verdict": "FAIL",
  "escalated": true
}

Stored automatically in:

runs.jsonl

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📂 REPOSITORY STRUCTURE

SiteSynapse/
│
├── agents/
│   ├── adjudicator_agent.py
│   ├── compliance_agent.py
│   ├── orchestrator.py
│   ├── report_agent.py
│   ├── state.py
│   └── vision_agent.py
│
├── eval/
│   ├── eval_harness.py
│   └── golden_set.py
│
├── llmops/
│   ├── logger.py
│   └── registry.py
│
├── rag/
│   ├── corpus.py
│   └── retriever.py
│
├── vision/
│   ├── defect_detector.py
│   ├── depth_estimator.py
│   └── measurements.py
│
├── tests/
│   └── test_regression.py
│
├── app.py
├── cli.py
├── generate_sample_images.py
├── requirements.txt
└── README.md

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⚡ QUICK START

1️⃣ Clone Repository

git clone https://github.com/yourusername/SiteSynapse.git

cd SiteSynapse

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2️⃣ Create Virtual Environment

Windows

python -m venv venv

venv\Scripts\activate

Linux / macOS

python3 -m venv venv

source venv/bin/activate

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3️⃣ Install Dependencies

pip install -r requirements.txt

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4️⃣ Generate Synthetic Inspection Images

python generate_sample_images.py

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5️⃣ Run CLI Inspection

python -m cli sample_images/wall_0.png

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6️⃣ Execute Regression Suite

python tests/test_regression.py

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7️⃣ Launch Streamlit Dashboard

streamlit run app.py

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📈 EVALUATION BENCHMARKS

Metric	Target
Retrieval Recall@3	≥ 0.85
Citation Grounding Rate	≥ 0.90
Depth Estimator MAE	≤ 1.5m
Escalation Precision	≥ 0.80
Report Completeness	≥ 95%
Compliance Accuracy	≥ 0.88

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🧪 SAMPLE INSPECTION OUTPUT

Defect:
  Wall Crack

Length:
  2.3 m

Depth:
  0.08 m

Compliance:
  FAIL

Citation:
  OSHA 1926.501

Confidence:
  0.37

Escalated:
  YES

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🛠 TECHNOLOGY STACK

Core Stack

Language:
  Python 3.10+

Frontend:
  Streamlit
  Custom CSS
  GSAP 3

Machine Learning:
  NumPy
  Scikit-Learn
  Pillow

RAG:
  TF-IDF Retrieval
  Hybrid Search

Testing:
  Pytest
  Evaluation Harness

Observability:
  JSONL Tracing
  Run Logging

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💼 RESUME HIGHLIGHT

Designed and developed a multi-agent AI property inspection platform integrating computer vision, retrieval-augmented generation, rule-based compliance validation, human-in-the-loop escalation workflows, and LLMOps evaluation pipelines to generate citation-grounded inspection reports with quantitative structural measurements and automated regulatory assessment.

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📚 CITATION

@software{sitesynapse2026,
  title={SiteSynapse},
  author={Your Name},
  year={2026},
  description={AI Property Inspection Copilot with Vision-Grounded Measurements, Compliance RAG, Multi-Agent Escalation and LLMOps Evaluation}
}

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📜 LICENSE

MIT License

Copyright (c) 2026

See the LICENSE file for full details.

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⚡ INSPECT SMARTER. MEASURE PRECISELY. COMPLY AUTOMATICALLY.

Built with Python • Computer Vision • RAG • Multi-Agent AI • Streamlit