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🔬 Analytiq is a premium, enterprise-grade AI-powered data analytics and business intelligence platform.

Rebuilt from the ground up as a production-ready application, it pairs a fast FastAPI Python backend with a highly interactive React 19 + TypeScript frontend. It features a draggable Power BI-style dashboard, multimodal RAG studio, auto-ML pipelines, statistical EDA, and auto-generated senior-analyst PDF reports.

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🗺️ Architectural Flow

Here is the high-level architecture and system flow of the Analytiq ecosystem:

graph TD
    subgraph Frontend [React 19 Frontend]
        UI["Vite & Tailwind Dashboard"] --> Auth["Auth Gate"]
        UI --> DS_Grid["Draggable Grid (react-grid-layout)"]
        DS_Grid --> Plotly["Plotly.js Dynamic Visualizations"]
        UI --> ChatUI["AI Chat Interface"]
        UI --> RAGStudio["RAG KB Management"]
    end

    subgraph Backend [FastAPI Backend]
        API["FastAPI Router"] --> Store["Dataset Store"]
        API --> Cleaner["Auto-Cleaner Engine"]
        API --> EDA["Deep EDA Engine"]
        API --> BI["BI Root Cause & Segment Engine"]
        API --> ML["ML AutoML Leaderboard"]
        API --> RAG["RAG Service"]
        API --> PDF["ReportLab PDF Builder"]
    end

    subgraph AI_Services [AI & LLM Services]
        RAG --> GeminiEmbed["Gemini embedding-004"]
        ChatUI --> ToolDispatch["Safe Tool Dispatcher"]
        ToolDispatch --> Groq["Groq (Llama 3.3)"]
        PDF --> Narrator["AI Chart Narrator"]
        Narrator --> Groq
    end

    subgraph Data_Sources [Data Sources & KB]
        CSV["CSV / Excel / JSON Files"] --> API
        Docs["PDF / DOCX / Spreadsheets / Media"] --> RAG
    end

    style Frontend fill:#0f172a,stroke:#3b82f6,stroke-width:2px,color:#fff
    style Backend fill:#0f172a,stroke:#8b5cf6,stroke-width:2px,color:#fff
    style AI_Services fill:#0f172a,stroke:#06b6d4,stroke-width:2px,color:#fff
    style Data_Sources fill:#1e293b,stroke:#475569,stroke-width:1px,color:#fff

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✨ Enterprise Features

Category	Feature	Description
📥 Smart Ingestion	Robust Upload	Handles CSV, Multi-Sheet Excel, and JSON files up to 200 MB with strict dirty-data tolerance.
🧹 Data Sanitization	Data Quality Hub	Instant per-column quality profiling, outlier detection, and one-click auto-cleaning with undo features.
📊 BI & Analytics	Power BI Dashboard	Draggable, resizable layout tiles, dynamic KPI strip, custom tile builder, and automatic cross-filtering.
🔬 Statistical Suite	Deep EDA	Normality testing, distribution fitting, time-series stationarity checks, ANOVA, and VIF calculations.
💡 Strategic Insights	Business Intel	Domain-aware insight cards (HR/Sales/E-commerce), Root Cause analysis, cohort tracking, and Pareto charts.
🤖 Automated ML	Predictive modeling	Auto task detection (Classification/Regression), CV leaderboard scoring, and feature importance mappings.
💬 AI Copilot	Safe Chat Agent	Plain-English query processor -> secure tool dispatcher (no code execution) -> interactive charts and tables.
🧠 Knowledge Store	RAG Studio	Custom local vector index ingestion of PDFs, DOCX, CSVs, and video/images via Gemini Vision embeddings.
📄 Executive Reports	Document Generator	Beautifully styled ReportLab PDF reports (cover page, TOC, benchmarks, and AI-narrated chart guides).

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🛠️ The Tech Stack

Backend ──── FastAPI · pandas · scikit-learn · scipy · statsmodels · ReportLab
Frontend ─── React 19 · TypeScript · Tailwind CSS 4 · Plotly.js · Zustand
AI ───────── Groq (Llama 3.3) · Google Gemini (Vision, Embeddings)
RAG ──────── Custom NumPy Vector Store · Gemini text-embedding-004
Deployment ─ Docker · Render / Railway

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🚀 Local Development

1. Backend Setup (Python 3.11+)

# Navigate to the backend directory
cd backend

# Install dependencies
pip install -r requirements.txt

# Create environment configuration
cp .env.example .env          # Update with your actual API keys

# Start the server (runs on http://localhost:8000)
uvicorn app.main:app --reload --port 8000

2. Frontend Setup (Node.js 20+)

# Navigate to the frontend directory
cd frontend

# Install node dependencies
npm install

# Start local development server (proxies /api to port 8000)
npm run dev

3. Running Automated Tests

Run the 37-point end-to-end integration and smoke test suite:

cd backend
python tests/smoke_test.py

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🔑 Configuration & Keys

The application remains fully functional locally without keys (AI modules degrade gracefully and show setup alerts). Add these to your .env to activate intelligence features:

Environment Variable	Source	Functionality
GROQ_API_KEY	console.groq.com	Powering the AI Chat copilot and chart generation narratives
GEMINI_API_KEY	aistudio.google.com	Powering image/video analysis, RAG indexing, and executive summaries

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☁️ Deployment Guides

Render Deployment

1. Push this code repository to GitHub.
2. Link your repository in Render and create a Web Service using the Blueprint configuration in render.yaml.
3. Set your environment keys (GROQ_API_KEY, GEMINI_API_KEY) in the Render Dashboard.

Railway Deployment

1. Initialize a new project on Railway from your repository.
2. Railway will automatically detect the root Dockerfile and build a multi-stage production container.
3. Configure variables and deploy.

Manual Docker Execution

# Build the container
docker build -t analytiq-platform .

# Run the container
docker run -p 8000:8000 \
  -e GROQ_API_KEY=your_key \
  -e GEMINI_API_KEY=your_key \
  -v analytiq-data:/srv/data \
  analytiq-platform

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📝 License

This software is subject to a proprietary license. Copyright © 2026 Shweta Mishra. All rights reserved. Unauthorized distribution, copying, or modification is prohibited.