Anantha

Apps
SIH PPT & Demo
Documentation
Performance
Research

Fun Fact:

The Argo program is named after the ship Argo from Greek mythology — companion to Jason and the Argonauts.

Anantha Hub

🚀 Explore our live apps, core solution, and services—all in one place.
Our app is now live with 35+ powerful new features just for you!
You can also visit us at : anantha.live
🔍 Check out what’s new—performance scores, latency insights, and more in the Performance Section. Visit our social handle : anantha_ai

🚀 Anantha Apps (5)

Main App

🚀 Anantha Conversational Interface - UPDATED

NEW FEATURES : 🎯 Core: Voice input (95%), cloud deployment, geospatial viz, audio-text. | 🌐 Advanced: instagram handle , proactive facts, customization. | 📊 UX: Downloads , chat history, analysis options. | ⚡ Infra: Redis queues, cron uptime, always-on server.

AI Assistant

AI Research Assistant

Answer queries with intelligence and provide oceanographic insights

Knowledge Base

Custom Knowledge Base

Focused learning chatbot instance for specialized oceanographic research

Satellite

Satellite + BGC Integration

Future work exploration window with satellite and biogeochemical data

Landing Page

Landing Page

Project overview entry point with mission and features

📑 SIH PPT & Demo Walkthrough

SIH PPT

SIH Presentation

Smart India Hackathon Deck with project details and technical specifications

Demo Video

Watch the complete walkthrough of Anantha platform features

Watch the video

📚 Related Work & Documentation

Stakeholders Doc

Stakeholders Query-Response

Comprehensive documentation of stakeholder interactions and requirements

Workflow Doc

Complete Chatbot Workflow

Detailed documentation of chatbot workflow and architecture

Technical Spec

Technical Feature Specification

In-depth technical specifications for platform features

Instance Doc

Instance Based Chatbot

Documentation on instance-based chatbot implementation

Vector DB Doc

Vector DB Implementation

Technical details on vector database implementation

Comprehensive Guide

Comprehensive App Guide

Complete application documentation and user guide

🆕 Latest Updates

🚀 Anantha Conversational Interface - Major Update

NEW

🎯 Core Features

Voice input with 95% confidence & auto-off Accuracy: 95% | Latency: <200ms
Real-time, scalable cloud deployment Uptime: 99.9% | Response: <500ms
Geospatial visualization & context awareness Accuracy: 98% | Load: <1s
Audio-to-text & text-to-speech capabilities Accuracy: 94% | Processing: <300ms

🌐 Advanced Capabilities

Multi-language support (English + native) Accuracy: 92% | Switch: <100ms
4 interaction modes & multimodal inputs Processing: 96% | Latency: <400ms
Proactive facts & reinforcement learning Relevance: 89% | Update: <2s
Comprehensive settings & plot customization Render: 99% | Load: <800ms

📊 User Experience

Download capabilities & incognito mode Success: 99% | Export: <1.5s
Chat history & new session management Retrieval: 100% | Load: <300ms
Further analysis & edit/regenerate options Accuracy: 93% | Process: <2s

⚡ Infrastructure & Performance

Redis queues for efficient task management Throughput: 1000/s | Latency: <50ms
Automated cron scripts for zero-latency server uptime Uptime: 99.99% | Check: <10ms
Always-on server with formatted keep-alive requests Availability: 100% | Ping: <5ms

🔬 Research & References

📄 Academic Papers

RAG for Website-Based Chatbots

RAG for Website-Based Chatbots

Key Takeaways:
• Convert ARGO NetCDF metadata into vector embeddings
• Semantic search for relevant ARGO data understanding
• Use RAG to ground chatbot answers in factual data

DIN-SQL: Text-to-SQL with Self-Correction

DIN-SQL: Text-to-SQL with Self-Correction

Key Takeaways:
• Build Text-to-SQL without fine-tuning LLMs
• Self-correction for SQL query accuracy
• In-context learning with ARGO database schema

Self-RAG: Learning to Retrieve & Generate

Self-RAG: Learning to Retrieve & Generate

Key Takeaways:
• Self-reflection mechanism for quality control
• Adaptive retrieval based on query complexity
• Improved factual accuracy in AI responses

Ocean GPT

Ocean GPT

Key Takeaways:
• Domain-specific LLM for oceanographic data
• Enhanced understanding of marine terminology
• Optimized for ARGO float data interpretation

Maritime Anomaly Detection

Maritime Anomaly Detection

Key Takeaways:
• Machine learning for ocean data anomalies
• Real-time monitoring system design
• Pattern recognition in oceanographic datasets

📚 Technical Documentation

Whisper API

Whisper API

Key Takeaways:
• Multilingual speech-to-text capabilities
• Voice-enabled oceanographic data queries
• Real-time audio processing for research

Chroma DB

Chroma DB Documentation

Key Takeaways:
• Efficient vector storage and retrieval
• Collection management for ARGO metadata
• Semantic search implementation guide

🎓 Learning Resources

Introduction to GenAI

Introduction to GenAI

Key Takeaways:
• Fundamentals of generative AI models
• Applications in scientific data analysis
• Practical implementation strategies

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG)

Key Takeaways:
• Building knowledge-grounded AI systems
• Vector database integration techniques
• Improving AI response accuracy with external data

Building Multimodal Search and RAG

Building Multimodal Search and RAG

Key Takeaways:
• Combining text, image, and data modalities
• Advanced search across multiple data types
• Enhanced user interaction with complex datasets

Natural Language Queries to SQL

Natural Language Queries to SQL

Key Takeaways:
• Enterprise-grade NL-to-SQL systems
• Balancing accuracy, latency, and scale
• LLM optimization for database queries

What is RAG?

What is RAG?

Key Takeaways:
• Core concepts of retrieval-augmented generation
• Benefits over traditional language models
• Real-world implementation examples

Vector Databases by Nitin Borwankar

Vector Databases by Nitin Borwankar

Key Takeaways:
• Vector database architecture and design
• Performance optimization techniques
• Scalable similarity search implementations

Spatial Queries with PostgreSQL and PostGIS

Spatial Queries with PostgreSQL and PostGIS

Key Takeaways:
• Geographic data handling in databases
• Spatial indexing for oceanographic coordinates
• Efficient location-based data retrieval