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📊 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."

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

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

Document History:

Version	Date	Author	Changes
1.0	January 2025	DataVision Team	Initial PRD