JumpIQ — Dealership Intelligence Engine

A Dealership Intelligence Engine that analyzes dealership data (revenue, valuation, market momentum, environment score) similar to stock market analysis, financial forecasting, and anomaly detection—but for dealerships.

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1. What This Project Is

Goal: Automatically detect patterns and problems in dealership data.

The platform already shows:

• Revenue trend lines
• Momentum vs Environment
• Quadrant classification (Champions / Stragglers / Opportunities)

The system goal is to detect patterns and anomalies and trace back reasons (real event vs seasonal vs data bug vs wrong model).

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2. Core Problem

Graphs can show sudden spikes or drops. The system must decide whether each change is:

#	Possibility	Example
1	Real business event	New product launch
2	Seasonal spike	Festival / holiday sales
3	Data bug	Wrong ETL or bad source
4	Wrong model	Incorrect algorithm output

Requirement: The system should detect anomaly and trace back reason.

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3. High-Level Pipeline

RAW DATA  →  Cleaning  →  Models  →  Derived Values  →  Rules  →  Database  →  UI

• Raw data: DMS, sales, financials, real estate valuation
• Cleaning: Missing data, wrong values, duplicates (e.g. imputation)
• Models: Trend, seasonality, anomaly detection, forecasting (time series)
• Derived values: Valuation Score, Profitability Score, Momentum Score, Market Health Score
• Rules: e.g. "if revenue growth > 20% and market stable → opportunity"
• Database: Store processed data
• UI: Dashboards, graphs, quadrants

Details: docs/PIPELINE.md

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4. Pattern Detection (Why Different Algorithms Matter)

Different algorithms detect different patterns:

Pattern	Example	Algorithms / Ideas
Trend	Revenue increasing steadily	Linear regression, slope
Seasonality	Oct/Nov spike every year	Seasonal decomposition, Prophet, ARIMA
Outliers	Sales 120 → 2 suddenly	Z-score, IQR, Isolation Forest
Event impact	Crash, fuel spike, policy	Event-based / causal analysis

Details: docs/PATTERN-DETECTION.md

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5. Multi-Agent Architecture (Proposed)

We are developing a Multi-Agent Architecture where specialized AI agents collaborate to investigate issues automatically.

graph TD
    A[Raw Sales Data] --> B[Data Pipeline Cleanup]
    B --> C[Super Agent]

    C -->|Requests Check| D(Trend Agent)
    C -->|Requests Check| E(Seasonality Agent)
    C -->|Requests Check| F(Outlier Agent)
    C -->|Requests Check| G(Market Context Agent)

    D -->|Returns Slope| C
    E -->|Returns Expected| C
    F -->|Returns Z-Score| C
    G -->|Returns Events| C

    C --> H{Is Anomaly?}
    H -->|Yes| I["Generate Explanation (e.g., 'Revenue dropped because...\\nTrend is negative + No Festival expected')"]
    H -->|No| J[Normal Operation / No Action]

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Agent	Job
Super Agent	Orchestrator. Triggers tests and compiles the final human-readable explanation.
Trend Agent	Analyzes steady growth / decline (Champions vs Stragglers)
Outlier Agent	Detects abnormal, unexpected spikes using Z-Scores & bounds
Seasonality Agent	Detects recurring, expected cycles (e.g., Holidays)
Market Agent	Checks external competitor trends and economic events

Why not neural networks: The system must explain its reasoning (e.g., "Revenue dropped because: inventory low, competitor price drop, no seasonal boost expected"). Black box neural networks fail at explainability. Therefore, we prefer interpretable models: regression, statistical rules, and explicit time series forecasting.

Details: docs/AGENTS.md

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6. Tools Agents Can Use

• Database — existing dealership metrics
• Web search — market trends
• MCP — tool integrations
• DMS — dealer management systems

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7. Phase 1 Task (Current Focus)

Directive: Understand algorithms deeply — not just run code.

1. Learn pattern detection
2. Learn time-series analysis
3. Learn anomaly detection
4. Send daily report

Study first: docs/PHASE1-STUDY.md

Start-to-end saare phases (Phase 1 → 6, evolving system tak): docs/ROADMAP.md

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8. Principles to Follow During Exploration

Do not jump straight to coding. Think like a data scientist/engineer:

• Understand patterns first (trend, seasonality, outliers, events), then algorithms.
• Always question the data — never trust raw data blindly; trace reasons for anomalies.
• Follow the pipeline: clean → validate → analyze (never run models on raw data only).
• Time-series thinking — revenue/month, valuation/quarter, momentum over time.
• Interpretable models — avoid black-box neural networks; prefer regression, rules, decomposition.
• Investigation mindset — system should explain why (e.g. "Revenue dropped because …").
• Relate everything to JumpIQ — every concept must answer: How does this help analyze dealership data?
• Daily reports — what you studied + how it applies to dealership revenue.

Full list (15 rules): docs/EXPLORATION-PRINCIPLES.md

Core rule: Understand → Analyze → Explain → Then Implement. Complete this understanding ASAP so we can implement.

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9. Tech Stack

Layer	Technology
Backend	Python, FastAPI, uvicorn
Frontend	Angular 21, TypeScript, ECharts (ngx-echarts)
Data	Pandas, NumPy
Models	statsmodels (seasonal decomposition), scikit-learn
Forecasting	Prophet (planned), ARIMA (planned)

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10. Project Layout

Dealership-Intelligence-Engine/
├── README.md                       # This file
├── requirements.txt                # Python deps for exploration scripts
├── data/                           # Sample raw data files
│   ├── helix_sample.csv
│   └── polk_sample.csv
│
├── docs/                           # Project documentation
│   ├── ROADMAP.md                  # Phase 1 → 6 roadmap
│   ├── PIPELINE.md                 # Data pipeline stages
│   ├── PATTERN-DETECTION.md        # Pattern types & algorithms
│   ├── AGENTS.md                   # Multi-agent architecture design
│   ├── EXPLORATION-PRINCIPLES.md   # 15 rules for thinking like a data scientist
│   ├── PHASE1-STUDY.md             # What to study first
│   ├── algorithms/                 # Algorithm deep-dive docs
│   │   ├── trend_detection.md      # Beginner's guide to trend detection
│   │   └── seasonality_detection.md # Beginner's guide to seasonality
│   └── learning/                   # Learning reference notes
│       ├── TREND_AND_SEASONALITY.md
│       └── ANOMALY_DETECTION.md
│
└── exploration/                    # Phase 1 experiments & PoC web app
    ├── README.md                   # Exploration guide
    ├── script/                     # Standalone learning scripts
    │   ├── trend_detection.py      # Trend: slope, moving avg, period comparison
    │   └── seasonality_detection.py # Seasonality: decomposition, residuals
    └── web app/                    # Working PoC dashboard
        ├── backend/                # FastAPI backend (Python)
        │   ├── main.py             # App entry point, CORS, router setup
        │   ├── requirements.txt    # Backend Python dependencies
        │   ├── data/               # Data source layer
        │   │   ├── __init__.py
        │   │   └── dealerships.py  # Mock dealership revenue data (DEALERSHIPS, MONTHS)
        │   ├── algorithms/         # Algorithm implementations
        │   │   ├── __init__.py
        │   │   ├── trend.py        # Slope, trend line, moving avg, classify
        │   │   └── seasonality.py  # Seasonal decomposition (statsmodels)
        │   └── routers/            # API route handlers
        │       ├── __init__.py
        │       ├── trends.py       # GET /api/trends, /api/trends/{dealership}
        │       └── seasonality.py  # GET /api/seasonality, /api/seasonality/{dealership}
        └── frontend/               # Angular 21 dashboard (TypeScript)
            ├── angular.json
            ├── package.json
            └── src/app/
                ├── app.ts                    # Root component
                ├── app.config.ts             # Providers (HttpClient, zone.js)
                ├── services/
                │   └── intelligence.service.ts # HTTP service + TypeScript interfaces
                ├── dashboard/
                │   ├── dashboard.component.ts    # Main dashboard logic
                │   ├── dashboard.component.html  # Template (cards, charts, tabs)
                │   └── dashboard.component.scss  # Styles
                └── charts/
                    ├── trend-chart.component.ts       # ECharts trend visualization
                    └── seasonality-chart.component.ts # ECharts seasonality visualization

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11. Running the PoC Web App

Backend (FastAPI)

cd exploration/web\ app/backend
pip install -r requirements.txt
uvicorn main:app --reload
# → http://localhost:8000

API endpoints:

Method	Endpoint	Description
GET	/	Health check + endpoint list
GET	/api/trends/	Trend data for all dealerships
GET	/api/trends/{dealership}	Trend data for one dealership
GET	/api/seasonality/	Seasonality for all dealerships
GET	/api/seasonality/{dealership}	Seasonality for one dealership

Frontend (Angular)

cd exploration/web\ app/frontend
npm install
npx ng serve
# → http://localhost:4200

The dashboard shows:

1. Classification cards — Each dealership labeled CHAMPION / STRAGGLER / AT RISK / RECOVERING
2. Revenue trend chart — Revenue lines + trend lines + 3-month moving averages (ECharts)
3. Seasonality decomposition — Observed / Trend / Seasonal / Residual breakdown per dealership

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12. How the Code Connects

Frontend (Angular)                    Backend (FastAPI)
─────────────────                     ─────────────────
intelligence.service.ts               main.py
  → GET /api/trends/        ───→        routers/trends.py
                                          → algorithms/trend.py
                                              → data/dealerships.py (DEALERSHIPS, MONTHS)
  → GET /api/seasonality/   ───→        routers/seasonality.py
                                          → algorithms/seasonality.py
                                              → data/dealerships.py (DEALERSHIPS, MONTHS)

dashboard.component.ts
  ← receives JSON ──────────────── ← returns { dealerships: [...] }
  → renders cards + charts

Data flow: dealerships.py → algorithm functions → router endpoints → Angular service → dashboard UI

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13. Why This Project Matters

This is high-level work across:

• Data engineering
• Data science
• System design
• AI agents
• Analytics pipelines

Very few backend engineers get this breadth. Use Phase 1 to build a strong foundation in algorithms and time series, then layer on agents and pipeline.