# 📊 DataVision AI - Product Requirements Document (PRD)

**Document Version:** 1.0  
**Last Updated:** January 2025  
**Product Name:** DataVision AI  
**Tagline:** *"Your data doesn't need dashboards. It needs intelligence."*

---

## 📋 Table of Contents

1. [Executive Summary](#1-executive-summary)
2. [Product Vision & Mission](#2-product-vision--mission)
3. [Target Audience & User Personas](#3-target-audience--user-personas)
4. [Core Features & Capabilities](#4-core-features--capabilities)
5. [Technical Architecture](#5-technical-architecture)
6. [API Reference](#6-api-reference)
7. [AI/ML Capabilities](#7-aiml-capabilities)
8. [User Journeys & Flows](#8-user-journeys--flows)
9. [Security & Compliance](#9-security--compliance)
10. [Success Metrics & KPIs](#10-success-metrics--kpis)
11. [Roadmap & Future Enhancements](#11-roadmap--future-enhancements)

---

## 1. Executive Summary

### 1.1 Product Overview

**DataVision AI** is an autonomous AI-powered business intelligence platform that transforms raw data into actionable executive insights. It combines state-of-the-art **AutoML** (Automated Machine Learning), **natural language AI chat**, and **intelligent reporting** into a unified "Business Analyst in a Box" experience.

### 1.2 Problem Statement

Traditional business intelligence tools require:
- Technical expertise to build dashboards and reports
- Data science knowledge for predictive modeling
- SQL skills for data querying
- Weeks of implementation time

**Result:** 73% of enterprise data goes unanalyzed, and business users remain dependent on technical teams.

### 1.3 Solution

DataVision AI democratizes data analysis by providing:

| Capability | Traditional BI | DataVision AI |
|------------|----------------|---------------|
| Dashboard Creation | Manual, hours | AI-generated, seconds |
| Predictive Models | Data science team | Zero-code AutoML |
| Data Queries | SQL required | Natural language |
| Reports | Manual creation | AI-automated |
| Time to Insight | Days/weeks | Minutes |

### 1.4 Key Differentiators

1. **Zero-Code AutoML** - Train production ML models without writing code
2. **Autonomous Dashboards** - AI designs the entire dashboard automatically
3. **7 AI Modes** - Different reasoning approaches for different analytical needs
4. **Real-time Prediction Playground** - Interactive predictions with instant results
5. **Multi-Agent System** - 40+ specialized AI agents for comprehensive analysis
6. **Advanced RAG System** - 3-tier retrieval for accurate, context-aware answers
7. **Enterprise-Ready** - Security, audit trails, scheduled reports

---

## 2. Product Vision & Mission

### 2.1 Vision Statement

> *"To make every business user a data-powered decision maker by removing technical barriers between humans and their data."*

### 2.2 Mission

Empower organizations to:
- **Analyze** data instantly through natural language
- **Predict** future outcomes with production-ready ML models
- **Automate** reporting and dashboard generation
- **Democratize** data science across all business functions

### 2.3 Core Value Propositions

| For Business Users | For Data Teams | For Executives |
|-------------------|----------------|----------------|
| Ask questions in plain English | Focus on complex problems | Instant executive summaries |
| Get instant visualizations | Reduce ad-hoc requests by 80% | AI-designed KPI dashboards |
| Make predictions without code | Production-ready model exports | Scheduled automated reports |
| Self-service analytics | Maintain data governance | Real-time business insights |

---

## 3. Target Audience & User Personas

### 3.1 Primary User Personas

#### 👤 Persona 1: Business Analyst (Sarah)

| Attribute | Details |
|-----------|---------|
| **Role** | Senior Business Analyst at mid-size company |
| **Technical Level** | Low (knows Excel, no SQL/Python) |
| **Pain Points** | Dependent on data team, slow report turnaround |
| **Goals** | Self-service analytics, faster insights |
| **Primary Features** | AI Chat, Reports, Dashboard |
| **Success Metric** | Reduce time-to-insight from days to minutes |

#### 👤 Persona 2: Data Scientist (Alex)

| Attribute | Details |
|-----------|---------|
| **Role** | Data Scientist at tech company |
| **Technical Level** | High (Python, ML frameworks) |
| **Pain Points** | Repetitive modeling tasks, stakeholder requests |
| **Goals** | Rapid prototyping, model comparison |
| **Primary Features** | AutoML, ML Predictions, Feature Engineering |
| **Success Metric** | Reduce model development time by 10x |

#### 👤 Persona 3: Executive (Michael)

| Attribute | Details |
|-----------|---------|
| **Role** | VP of Operations at enterprise |
| **Technical Level** | None |
| **Pain Points** | Outdated reports, no real-time visibility |
| **Goals** | Real-time KPIs, predictive insights |
| **Primary Features** | Executive Reports, Autonomous Dashboard |
| **Success Metric** | Data-driven decision making |

#### 👤 Persona 4: Product Manager (Priya)

| Attribute | Details |
|-----------|---------|
| **Role** | Product Manager at SaaS startup |
| **Technical Level** | Medium (can read data, limited SQL) |
| **Pain Points** | Understanding user trends, forecasting |
| **Goals** | User segmentation, feature impact analysis |
| **Primary Features** | Forecasting, Anomaly Detection, Chat |
| **Success Metric** | Identify growth opportunities |

### 3.2 Key Use Cases

| Use Case | Description | User Persona |
|----------|-------------|--------------|
| **Quick Data Analysis** | Drop CSV → Get instant insights | Business Analyst |
| **Predictive Modeling** | Train ML models without code | Data Scientist |
| **Automated Reporting** | Generate executive reports on demand | Executive |
| **Conversational Analytics** | Ask natural language questions | All |
| **Revenue Forecasting** | Predict future metrics | Product Manager |
| **Anomaly Detection** | Find outliers and unusual patterns | Operations |
| **Customer Segmentation** | Auto-cluster users/customers | Marketing |
| **Root Cause Analysis** | Understand "why" metrics changed | Analyst |

---

## 4. Core Features & Capabilities

### 4.1 Data Management (DataHub)

The central hub for all data operations.

| Feature | Description | Status |
|---------|-------------|--------|
| **Multi-format Upload** | CSV, Excel (.xlsx, .xls), JSON, PDF, DOCX, TXT, Images | ✅ Live |
| **Auto Data Quality Fix** | One-click data cleaning (missing values, outliers, duplicates) | ✅ Live |
| **Smart Column Detection** | Automatic target column detection for ML | ✅ Live |
| **Google Sheets Import** | Direct import from Google Sheets URLs | ✅ Live |
| **Multi-file Training** | Combine multiple files for unified training | ✅ Live |
| **Upload Cancellation** | Cancel in-progress uploads with cleanup | ✅ Live |
| **File Size Limit** | 100MB per file | ✅ Live |
| **Data Profiling** | Automatic statistical summaries | ✅ Live |
| **Currency Detection** | Auto-detect INR, USD, EUR, GBP, etc. | ✅ Live |

### 4.2 AutoML Training System

Production-ready machine learning without code.

#### Training Modes

| Mode | Algorithms | Duration | Use Case |
|------|------------|----------|----------|
| **Fast Mode** | 7 algorithms | 30-60 seconds | Quick prototyping |
| **Ultra Mode** | 20+ algorithms | 2-10 minutes | Production models |

#### Supported Algorithms

**Classification (15+ algorithms):**
- XGBoost Classifier
- LightGBM Classifier
- CatBoost Classifier
- Random Forest Classifier
- Extra Trees Classifier
- Gradient Boosting Classifier
- AdaBoost Classifier
- Logistic Regression
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- Neural Network (MLP)
- Naive Bayes (Gaussian, Multinomial)
- Decision Tree Classifier
- Bagging Classifier

**Regression (15+ algorithms):**
- XGBoost Regressor
- LightGBM Regressor
- CatBoost Regressor
- Random Forest Regressor
- Extra Trees Regressor
- Gradient Boosting Regressor
- Ridge Regression
- Lasso Regression
- ElasticNet
- Bayesian Ridge
- Huber Regressor
- Poisson Regressor
- Quantile Regressor
- Neural Network (MLP)

**Clustering:**
- K-Means
- DBSCAN
- Agglomerative Clustering
- Spectral Clustering
- Gaussian Mixture Model
- MeanShift

#### AutoML Features

| Feature | Description |
|---------|-------------|
| **Auto Task Detection** | Classification, Regression, Clustering auto-detection |
| **Feature Engineering** | 50+ synthetic features generation |
| **Hyperparameter Optimization** | Bayesian optimization with Optuna |
| **Ensemble Methods** | Stacking, Voting, Blending |
| **GPU Acceleration** | Auto GPU/CPU detection (CUDA, ROCm, Metal) |
| **Class Imbalance Handling** | SMOTE, class weights |
| **NLP Pipeline** | TF-IDF, Sentiment, Text Stats for text columns |
| **Cross-Validation** | Stratified K-Fold for robust evaluation |
| **Training Stop** | Cancel training mid-process |

### 4.3 ML Predictions & Analysis

Interactive prediction and model analysis tools.

| Feature | Description |
|---------|-------------|
| **Prediction Playground** | Interactive sliders for real-time predictions |
| **Batch Predictions** | Predict on entire datasets |
| **Model Explainability** | SHAP values and feature contributions |
| **Confusion Matrix** | Classification performance visualization |
| **ROC/PR Curves** | Model performance curves |
| **Feature Importance** | Visual ranking of predictive features |
| **What-If Analysis** | Scenario planning with predictions |
| **Model History** | Version control with rollback |
| **Model Persistence** | Save, load, delete trained models |

### 4.4 Autonomous Dashboard

AI-designed dashboards that build themselves.

| Feature | Description |
|---------|-------------|
| **AI-Designed Layout** | LLM decides KPIs, charts, colors automatically |
| **15+ Chart Types** | Bar, Line, Pie, Scatter, Heatmap, Sunburst, Treemap, etc. |
| **Dynamic KPIs** | Auto-calculated business metrics |
| **Domain Detection** | Auto-detect data domain (sales, finance, HR, etc.) |
| **Real-time Updates** | Dashboard refreshes when data changes |
| **Multiple Views** | Grid and List display modes |
| **Chart Interactions** | Hover, zoom, expand capabilities |

### 4.5 AI Analyst Chat

Conversational analytics powered by LLMs.

| Feature | Description |
|---------|-------------|
| **Natural Language Queries** | Ask anything about your data |
| **7 AI Modes** | Analyst, DeepThink, Vision, Predict, Agent, RAG, MCP |
| **ChatGPT-style Typing** | Word-by-word response animation |
| **Interactive Charts** | Plotly charts generated from queries |
| **Voice Input** | Microphone support for queries |
| **File Attachments** | Attach files directly in chat |
| **Conversation History** | Per-user conversation memory |
| **Smart Suggestions** | Follow-up question recommendations |
| **Confidence Scoring** | Answer reliability indicators |

#### AI Chat Modes

| Mode | Icon | Description | Best For |
|------|------|-------------|----------|
| **Analyst** | 📊 | Business analyst-style insights | General analysis |
| **DeepThink** | 🧠 | Multi-step chain-of-thought reasoning | Complex questions |
| **Vision** | 👁️ | Data visualization focus | Charts and graphs |
| **Predict** | 🔮 | ML predictions and forecasts | Future predictions |
| **Agent** | 🤖 | Autonomous multi-agent orchestration | Complex tasks |
| **RAG** | 📚 | Retrieval-augmented generation | Document Q&A |
| **MCP** | 🔗 | Model Context Protocol | Tool integration |

### 4.6 Reports Generation

Automated, AI-powered business reports.

| Report Type | Description | Output |
|-------------|-------------|--------|
| **Metrics Analysis** | Numeric data trends and patterns | Text + Charts |
| **Data Breakdown** | Category distributions and segments | Text + Charts |
| **Executive Summary** | High-level insights for leadership | PDF/Text |
| **Predictive Report** | ML forecasts (requires trained model) | Text + Charts |
| **Anomaly Report** | Outlier and unusual pattern detection | Text + Charts |

**Export Options:**
- PDF Export (branded, formatted)
- Text Export (downloadable)
- Multi-currency formatting (INR, USD, EUR, GBP)

### 4.7 Advanced RAG System

Three-tier retrieval-augmented generation for accurate answers.

| Tier | Features |
|------|----------|
| **Tier 1 (Standard)** | Query decomposition, MMR reranking, answer evaluation |
| **Tier 2 (Advanced)** | HyDE, Corrective RAG, Self-Reflection |
| **Tier 3 (Agentic)** | Tool-using agents, Multi-source retrieval, RRF fusion |

**RAG Components:**
- FAISS Vector Store for semantic search
- Knowledge Graph for relationship mapping
- Semantic query caching
- Context window optimization

---

## 5. Technical Architecture

### 5.1 System Architecture

```
┌─────────────────────────────────────────────────────────────────────┐
│                         FRONTEND (React 18)                         │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐  │
│  │ DataHub  │ │ AutoML   │ │ Dashboard│ │ AI Chat  │ │ Reports  │  │
│  └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘  │
└───────┼────────────┼────────────┼────────────┼────────────┼────────┘
        │            │            │            │            │
        └────────────┴────────────┴────────────┴────────────┘
                                  │
                    ┌─────────────▼─────────────┐
                    │    API GATEWAY (FastAPI)   │
                    │   Rate Limiting | Auth     │
                    └─────────────┬─────────────┘
                                  │
        ┌───────────────┬─────────┴─────────┬───────────────┐
        │               │                   │               │
┌───────▼───────┐ ┌─────▼─────┐ ┌───────────▼───────────┐ ┌─▼──────────┐
│  Data Service │ │ ML Engine │ │    AI Agent System    │ │  RAG Core  │
│  - Ingestion  │ │ - AutoML  │ │  - 40+ Specialized    │ │  - FAISS   │
│  - Quality    │ │ - Train   │ │    Agents             │ │  - Graph   │
│  - Transform  │ │ - Predict │ │  - Orchestrator       │ │  - Cache   │
└───────┬───────┘ └─────┬─────┘ └───────────┬───────────┘ └────────────┘
        │               │                   │
        └───────────────┴───────────────────┘
                        │
        ┌───────────────┼───────────────┐
        │               │               │
┌───────▼───────┐ ┌─────▼─────┐ ┌───────▼───────┐
│   File Store  │ │   Models  │ │   Supabase    │
│ /storage/data │ │  /models  │ │  PostgreSQL   │
└───────────────┘ └───────────┘ └───────────────┘
```

### 5.2 Technology Stack

| Layer | Technologies |
|-------|-------------|
| **Frontend** | React 18, Vite, TypeScript, Tailwind CSS, Framer Motion |
| **UI Components** | Radix UI, Headless UI, Lucide Icons |
| **Charts** | Plotly.js, Recharts, ApexCharts |
| **Backend** | FastAPI, Python 3.11+, Uvicorn, Pydantic |
| **ML Framework** | Scikit-learn, XGBoost, LightGBM, CatBoost, Optuna |
| **AI/LLM** | OpenAI GPT-4, Groq, Claude, OpenRouter |
| **Vector DB** | FAISS |
| **Database** | Supabase (PostgreSQL) |
| **Authentication** | Supabase Auth (JWT) |
| **Email** | Resend API |
| **Deployment** | Docker, HuggingFace Spaces |
| **File Processing** | Pandas, PyMuPDF, python-docx, Pillow |

### 5.3 Backend Structure

```
backend/
├── main.py                 # FastAPI application entry point
├── api/
│   └── v1/
│       └── endpoints/      # All API endpoint handlers
├── agents/                 # 40+ AI agents
│   ├── orchestrator.py     # Multi-agent coordinator
│   ├── universal_agent.py  # Query router
│   ├── forecast_agent.py   # Forecasting
│   ├── memory_agent.py     # Conversation memory
│   └── ...
├── core/                   # Business logic engines
│   ├── autonomous_data_ops.py
│   ├── export_engine.py
│   ├── data_grounding.py
│   └── ...
├── ml/                     # ML pipeline components
│   ├── automl_engine.py
│   ├── feature_engineer.py
│   └── ...
├── graph/                  # Knowledge graph
├── vector/                 # FAISS vector store
├── ingestion/              # Data ingestion pipeline
├── services/               # External services (email, etc.)
├── scheduler/              # Scheduled reports
└── utils/                  # Utility functions
```

### 5.4 Frontend Structure

```
frontend/
├── src/
│   ├── pages/              # 18 main pages
│   │   ├── Landing.tsx     # Landing page
│   │   ├── DataHub.tsx     # Data management
│   │   ├── AutoML.tsx      # ML training
│   │   ├── MLPredictions.tsx
│   │   ├── AutonomousDashboard.tsx
│   │   ├── AnalystChat.tsx
│   │   ├── Reports.tsx
│   │   └── ...
│   ├── components/         # Reusable UI components
│   ├── services/           # API client
│   ├── store/              # Zustand state management
│   ├── contexts/           # React contexts
│   └── lib/                # Utilities
└── public/                 # Static assets
```

---

## 6. API Reference

### 6.1 Authentication APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v1/email-prefs/auth/request-password-reset` | POST | Request password reset email |
| `/api/v1/email-prefs/auth/update-password-with-token` | POST | Update password with token |

### 6.2 Data Management APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v1/upload/` | POST | Upload files |
| `/api/v1/files/` | GET | List user files |
| `/api/v1/files/{filename}` | DELETE | Delete file |
| `/api/v1/files/cancel-upload` | POST | Cancel upload |
| `/api/v2/data/quality-check` | POST | Check data quality |
| `/api/v2/data/auto-fix` | POST | Auto-fix data issues |
| `/api/v1/upload/google-sheets` | POST | Import from Google Sheets |

### 6.3 Analytics APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v1/analytics/overview` | GET | Get analytics overview |
| `/api/v1/unified-analytics/` | POST | Unified analytics engine |
| `/api/v1/universal/` | POST | Universal AI agent |
| `/api/v1/chat/` | POST | AI chat endpoint |
| `/api/v1/autonomous/dashboard` | POST | Generate dashboard |
| `/api/v1/autonomous/auto-analyze` | POST | Auto-analyze file |

### 6.4 AutoML APIs (v2)

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v2/automl/train` | POST | Fast mode training |
| `/api/v2/automl/ultra-train` | POST | Ultra mode training |
| `/api/v2/automl/predict` | POST | Make predictions |
| `/api/v2/automl/batch-predict` | POST | Batch predictions |
| `/api/v2/automl/charts` | GET | Get ML charts |
| `/api/v2/automl/stop` | POST | Stop training |
| `/api/v2/automl/models` | GET | List models |
| `/api/v2/automl/models/{id}` | DELETE | Delete model |
| `/api/v2/automl/rollback` | POST | Rollback model |

### 6.5 Prediction Playground APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v2/playground/config` | GET | Get playground config |
| `/api/v2/playground/predict` | POST | Playground prediction |
| `/api/v2/playground/shap` | POST | SHAP explanation |

### 6.6 Reports APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v1/reports/generate` | POST | Generate report |
| `/api/v1/reports/export/pdf` | POST | Export PDF |
| `/api/v1/reports/scheduled` | POST | Schedule report |

### 6.7 Storage APIs

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/v1/autonomous/models/{user_id}` | GET | List user models |
| `/api/v1/autonomous/models/{user_id}` | DELETE | Delete all models |

---

## 7. AI/ML Capabilities

### 7.1 AI Agent System

DataVision AI employs a sophisticated multi-agent architecture with 40+ specialized agents.

#### Core Agents

| Agent | File | Purpose |
|-------|------|---------|
| **Universal Agent** | `universal_agent.py` | NLU query routing and intent detection |
| **Orchestrator** | `orchestrator.py` | Multi-agent coordination |
| **Query Classifier** | `query_classifier.py` | Intent classification |
| **Query Dispatcher** | `query_dispatcher.py` | Route to appropriate handler |
| **Response Enhancer** | `response_enhancer.py` | Answer quality improvement |
| **Confidence Scorer** | `confidence_scorer.py` | Answer reliability scoring |
| **Memory Agent** | `memory_agent.py` | Conversation context management |

#### Analytics Agents

| Agent | File | Purpose |
|-------|------|---------|
| **Forecast Agent** | `forecast_agent.py` | Revenue and metric forecasting |
| **Deep Research Agent** | `core/deep_research_agent.py` | Multi-step research |
| **Data Quality Agent** | `data_quality.py` | Data validation and cleaning |
| **Visualization Agent** | `visualization.py` | Chart generation |

#### ML Pipeline Agents

| Agent | File | Purpose |
|-------|------|---------|
| **Feature Engineer** | `feature_engineer.py` | Feature creation |
| **Model Strategy Agent** | `model_strategy.py` | Algorithm selection |
| **Hyperparameter Agent** | `hyperparam.py` | Optimization |
| **Training Validator** | `training_validator.py` | Training validation |
| **Evaluation Agent** | `evaluation.py` | Model evaluation |
| **Deployment Agent** | `deployment.py` | Production preparation |

#### Chart Agents

| Agent | File | Purpose |
|-------|------|---------|
| **Chart Gatekeeper** | `chart_gatekeeper.py` | Chart validation |
| **Smart Chart** | `smart_chart.py` | LLM-driven chart generation |
| **Premium Charts** | `premium_charts.py` | Advanced visualizations |
| **Autonomous Charts** | `autonomous_charts.py` | Auto dashboard charts |

### 7.2 LLM Integration

| Provider | Models | Use Case |
|----------|--------|----------|
| **OpenAI** | GPT-4, GPT-4-turbo, GPT-3.5-turbo | Primary inference |
| **Groq** | Llama 3, Mixtral | Fast inference |
| **Claude** | Claude 3, Claude 3.5 | Complex reasoning |
| **OpenRouter** | Multi-model routing | Fallback and routing |

### 7.3 ML Model Performance Targets

| Task | Metric | Target | Notes |
|------|--------|--------|-------|
| Classification | Accuracy | >85% | With hyperparameter tuning |
| Classification | F1-Score | >0.80 | For imbalanced datasets |
| Regression | R² | >0.75 | For well-structured data |
| Regression | RMSE | <15% of range | Normalized |
| Clustering | Silhouette | >0.5 | For clear clusters |

---

## 8. User Journeys & Flows

### 8.1 New User Onboarding Flow

```
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Landing    │───▶│    Signup    │───▶│   Confirm    │
│    Page      │    │    Page      │    │    Email     │
└──────────────┘    └──────────────┘    └──────────────┘
                                               │
                                               ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   DataHub    │◀───│   Tutorial   │◀───│    Login     │
│ (Upload CSV) │    │  (Optional)  │    │    Page      │
└──────────────┘    └──────────────┘    └──────────────┘
```

### 8.2 Data Analysis Flow

```
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Upload     │───▶│   Quality    │───▶│   Auto-Fix   │
│    File      │    │    Check     │    │  (Optional)  │
└──────────────┘    └──────────────┘    └──────────────┘
                                               │
        ┌──────────────────────────────────────┤
        │                                      │
        ▼                                      ▼
┌──────────────┐                      ┌──────────────┐
│   AI Chat    │                      │   AutoML     │
│   Analysis   │                      │   Training   │
└──────────────┘                      └──────────────┘
        │                                      │
        ▼                                      ▼
┌──────────────┐                      ┌──────────────┐
│   Reports    │                      │ Predictions  │
│  Generation  │                      │  Playground  │
└──────────────┘                      └──────────────┘
```

### 8.3 AutoML Training Flow

```
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Select     │───▶│   Choose     │───▶│   Configure  │
│    File      │    │   Target     │    │    Mode      │
└──────────────┘    └──────────────┘    └──────────────┘
                                               │
                                               ▼
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│    Model     │◀───│  Training    │◀───│   Start      │
│   Results    │    │  Progress    │    │  Training    │
└──────────────┘    └──────────────┘    └──────────────┘
        │
        ├───────────────┬───────────────┐
        ▼               ▼               ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│  Playground  │ │   Charts     │ │  Predictions │
│  Prediction  │ │   (ROC/CM)   │ │    Batch     │
└──────────────┘ └──────────────┘ └──────────────┘
```

### 8.4 Dashboard Generation Flow

```
┌──────────────┐    ┌──────────────┐    ┌──────────────┐
│   Select     │───▶│     AI       │───▶│   Display    │
│    File      │    │   Analyzes   │    │  Dashboard   │
└──────────────┘    └──────────────┘    └──────────────┘
                          │
                          │ Auto-generates:
                          ├── KPI Cards
                          ├── Chart Types
                          ├── Color Schemes
                          └── Layout
```

---

## 9. Security & Compliance

### 9.1 Authentication & Authorization

| Feature | Implementation |
|---------|---------------|
| **User Authentication** | Supabase Auth with JWT |
| **Password Policy** | Minimum 8 characters |
| **Password Reset** | Custom branded email flow |
| **Session Management** | JWT with refresh tokens |
| **Rate Limiting** | Per-user request limits |

### 9.2 Data Security

| Feature | Implementation |
|---------|---------------|
| **Data Isolation** | Per-user file and model storage |
| **File Sanitization** | Filename sanitization, path traversal prevention |
| **Input Validation** | Pydantic models for all inputs |
| **Secure Headers** | XSS, CSRF protection |
| **AI Security** | Prompt injection detection |

### 9.3 Data Privacy

| Aspect | Approach |
|--------|----------|
| **Data Storage** | User files stored in isolated directories |
| **Data Retention** | User-controlled deletion |
| **Data Processing** | In-memory processing, no permanent storage of analysis |
| **Model Storage** | User-isolated model directories |

### 9.4 Enterprise Security Features

| Feature | Description |
|---------|-------------|
| **Audit Logging** | Action tracking for compliance |
| **Secure Exports** | Validated export endpoints |
| **File Type Validation** | Whitelist-based file type checking |
| **Size Limits** | 100MB per file upload |

---

## 10. Success Metrics & KPIs

### 10.1 Product Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| **Time to First Insight** | Time from upload to first analysis | < 2 minutes |
| **Model Training Time (Fast)** | Fast mode training duration | < 60 seconds |
| **Model Training Time (Ultra)** | Ultra mode training duration | < 10 minutes |
| **Dashboard Generation Time** | Time to generate dashboard | < 30 seconds |
| **Chat Response Time** | AI response latency | < 3 seconds |

### 10.2 Quality Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| **ML Model Accuracy** | Classification accuracy | > 85% |
| **Chat Answer Quality** | User satisfaction score | > 4.0/5.0 |
| **Report Completeness** | Coverage of key insights | > 90% |
| **Dashboard Relevance** | Chart appropriateness score | > 85% |

### 10.3 Usage Metrics

| Metric | Description | Tracking |
|--------|-------------|----------|
| **DAU/MAU** | Active users | Analytics |
| **Files Uploaded** | Data engagement | Database |
| **Models Trained** | ML adoption | Database |
| **Reports Generated** | Reporting usage | Database |
| **Chat Queries** | AI engagement | Logs |

### 10.4 Business Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| **User Retention (D7)** | 7-day retention | > 40% |
| **User Retention (D30)** | 30-day retention | > 25% |
| **Feature Adoption** | % using AutoML | > 30% |
| **Session Duration** | Average session time | > 10 minutes |

---

## 11. Roadmap & Future Enhancements

### 11.1 Current State (v1.0) ✅

| Feature | Status |
|---------|--------|
| Data Upload & Management | ✅ Complete |
| AutoML (Fast/Ultra) | ✅ Complete |
| ML Predictions & Playground | ✅ Complete |
| Autonomous Dashboard | ✅ Complete |
| AI Analyst Chat (7 Modes) | ✅ Complete |
| Reports Generation | ✅ Complete |
| User Authentication | ✅ Complete |
| Dark/Light Theme | ✅ Complete |

### 11.2 Near-term Roadmap (Q1-Q2 2025)

| Feature | Priority | Description |
|---------|----------|-------------|
| **Database Connectors** | High | Direct SQL/PostgreSQL/MySQL connections |
| **API Data Sources** | High | REST API data ingestion |
| **Collaborative Workspaces** | Medium | Team sharing and collaboration |
| **Custom Dashboards** | Medium | User-designed dashboard layouts |
| **Scheduled Reports** | Medium | Email reports on schedule |
| **Model API Export** | Medium | REST API for trained models |

### 11.3 Mid-term Roadmap (Q3-Q4 2025)

| Feature | Priority | Description |
|---------|----------|-------------|
| **Real-time Data Streams** | High | Kafka/streaming data support |
| **Advanced Forecasting** | High | Prophet, ARIMA, neural forecasting |
| **Natural Language to SQL** | Medium | Text-to-SQL queries |
| **Embedded Analytics** | Medium | Embed dashboards in other apps |
| **White-label Solution** | Low | Custom branding options |

### 11.4 Long-term Vision (2026+)

| Feature | Description |
|---------|-------------|
| **AutoML v3** | Neural architecture search, AutoML for deep learning |
| **Real-time Anomaly Alerts** | Streaming anomaly detection |
| **Predictive Actions** | Auto-trigger actions based on predictions |
| **Multi-tenant Enterprise** | Full enterprise deployment |
| **On-premise Deployment** | Self-hosted enterprise version |

---

## 12. Appendix

### 12.1 Glossary

| Term | Definition |
|------|------------|
| **AutoML** | Automated Machine Learning - auto model selection and tuning |
| **RAG** | Retrieval-Augmented Generation - AI with document context |
| **SHAP** | SHapley Additive exPlanations - model interpretability |
| **KPI** | Key Performance Indicator |
| **LLM** | Large Language Model |
| **FAISS** | Facebook AI Similarity Search - vector database |

### 12.2 Technical Requirements

**Minimum Server Requirements:**
- CPU: 4 cores
- RAM: 8GB (16GB recommended)
- Storage: 50GB SSD
- Python: 3.11+
- Node.js: 18+

**Supported Browsers:**
- Chrome 90+
- Firefox 90+
- Safari 14+
- Edge 90+

### 12.3 Contact & Support

| Resource | Link |
|----------|------|
| **Production URL** | https://killerkumar-ai-business-analyst.hf.space |
| **Documentation** | This PRD |
| **Support Email** | insights@ai20insights.tech |

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**Document History:**

| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | January 2025 | DataVision Team | Initial PRD |

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