Artificial Intelligence and Business Automation

NovaMind AI

Multilingual Conversational AI Platform

NovaMind AI is a multilingual AI assistant that supports voice and text conversations in English, Hindi, and Hinglish.

SingaporeMarket

2 MonthsTimeline

3 Members (2 AI Developers + 1 Full-Stack Developer)Team

Artificial Intelligence and Business AutomationIndustry

Project Overview

NovaMind AI is a multilingual AI assistant that supports voice and text conversations in English, Hindi, and Hinglish.

Users can speak or type their questions. The platform identifies the language and gives a suitable answer. It also remembers previous messages, so users can continue the same conversation without repeating the details.

OpenAI, RAG, and LangChain help the platform understand questions, find useful information, and give answers based on earlier messages. It works smoothly on both desktop and mobile devices.

What We Built

We built a voice and text-based AI platform that supports English, Hindi, and Hinglish conversations. The platform includes automatic language detection, real-time speech-to-text conversion, AI-generated responses, voice playback, conversation history, session management, RAG-based knowledge retrieval, secure authentication, and responsive layouts. Backend APIs, MongoDB Atlas connections, OpenAI integration, LangChain workflows, Python-based AI processing, security controls, and real-time response handling were also developed to run the platform smoothly.

Client Background

Albert Joe works in the technology and AI solutions space. He wanted to develop an AI platform that could communicate with users in more than one language. Many users speak in English, Hindi, or Hinglish during everyday conversations. Basic chatbots often struggle when users switch between these languages or ask follow-up questions based on earlier messages. He needed a platform where users could speak or type, switch languages naturally, and receive helpful answers based on previous conversations and business-related information.

Project Scope

We worked on the complete project, from planning and design to deployment, including:

Requirement gathering
Conversation and user-flow planning
Voice, text, and multilingual interaction planning
UI/UX design
Frontend development
Backend and REST API development
User authentication
MongoDB Atlas setup
OpenAI, RAG, and LangChain implementation
Python-based AI processing
Conversation memory
Speech-to-text and text-to-speech setup
Real-time response streaming
Performance optimisation
Security implementation
Cross-browser and device testing
VPS configuration, deployment, and live review

Target Audience

NovaMind AI is built for businesses that want to use AI for customer support or daily internal work. The main users include:

Product teams
Customer support teams
Operations teams
Service-based businesses
SaaS companies
Enterprises
Businesses serving English, Hindi, and Hinglish-speaking users

Objectives

The main objectives were to:

01Create an AI platform for voice and text conversations
02Support conversation in English, Hindi, and Hinglish
03Identify the user’s language automatically
04Allow users to switch languages during a conversation
05Give answers based on previous messages
06Save and manage conversation history
07Find useful information from connected business data
08Provide fast, real-time responses
09Avoid repeated or unrelated answers
10Protect user accounts and conversation data
11Support future integrations and new AI features

Business Challenge

Before development, we looked at the main problems users face when talking to AI in different languages.

01Basic chatbots often struggle when users switch between languages. Sometimes, users have to select a language manually or repeat their message.
02Voice conversations were also more complex. The system had to convert speech into text, understand the language and question, and then give the reply in voice.
03The platform also needed to remember earlier messages so users could ask follow-up questions without repeating the same details.
04The main challenge was to bring language detection, voice support, conversation memory, and useful AI responses into one simple platform.

Our Solution

We developed a multilingual AI platform for voice and text conversations.

The platform identifies English, Hindi, and Hinglish automatically, so users do not need to select a language manually.
For voice input, speech is converted into text. The system then understands the question and gives the reply in both text and voice.
It also remembers earlier messages, allowing users to continue the conversation without repeating the same details.
Conversation history is saved in MongoDB Atlas. OpenAI uses previous messages and connected business information to give helpful answers. The response appears gradually on the screen, making the conversation feel faster and smoother.

Key Features

The platform includes the following features:

03Voice and Text Conversations

Voice and Text Conversations

Chat through voice or text
Switch between speaking and typing
Receive replies in text or voice

Development Process

The project began with a discussion about the client’s goals and the kind of experience expected from the platform. We studied how users would communicate through voice and text, what language support was needed, and how earlier conversations should be handled. This gave the project a clear direction.

Technical Challenges

Understanding multilingual messages

A custom system checked scripts, common words, Roman Hindi, mixed-language messages, and language changes during conversations.

Processing voice conversations

Speech-to-text, language detection, AI response generation, and voice playback were connected in one flow.

Remembering earlier conversations

Previous messages were saved and used for follow-up questions.

Managing long conversation history

Only useful earlier messages were added to new requests to keep responses fast.

Finding useful business information

The RAG system searched connected business data before preparing the answer.

Improving response speed

Responses were shown in real time, and unnecessary processing was reduced.

Protecting private conversations

Secure login, protected APIs, input validation, and session controls were added.

Handling failed requests

Error messages, logs, and request limits were added.

Technology Stack

Frontend

Next.js

Backend

Express.js

Backend

REST APIs

Database

MongoDB Atlas

AI Integration

OpenAI

AI Orchestration

LangChain

Knowledge Retrieval

Retrieval-Augmented Generation

AI Processing

Python

Authentication

JWT Authentication

Real-Time Communication

WebSockets

Deployment

Ubuntu VPS

Deployment

Nginx

Deployment

PM2

Deployment

SSL

Version Control

Git and GitHub

Design Decisions

The platform was designed to keep voice and text conversations simple and easy to follow. Important decisions included:

Conversation-focused interface
Voice and text controls in one place
Clear user and AI messages
Easy access to previous conversations
Visible listening and processing states
Automatic language detection
Real-time response display
Simple login and account flow
Responsive layouts across devices
Clear error and retry messages
Only essential controls in the chat area
Easy voice playback for generated responses

SEO and Indexing

NovaMind AI includes a public website and private user conversation pages. SEO work focused on the public website, while private account and conversation pages were kept out of search results. The work included:

Clear page titles
Proper page headings
Relevant AI platform keywords
Multilingual AI-related content
Voice assistant-related content
Clean page URLs
Meta descriptions
Mobile-friendly layouts
Image optimisation
Fast public-page loading
Restricted indexing for private pages
Clear feature and technology sections

Performance Optimisation

We improved the platform’s performance through:

Real-time AI response streaming
Fast language detection
Controlled use of previous messages
Reduced duplicate AI requests
Reusable Next.js components
Optimised backend APIs and lighter frontend code
Faster MongoDB searches
Conversation pagination
Better RAG document organisation
Caching for non-sensitive information
Fast WebSocket communication
Smooth voice processing
Clear loading and error states
Proper failed-request handling
PM2 monitoring with automatic restarts

Security

The platform handles private user and business information, so security was important throughout development. Security measures included:

Secure login with password hashing
JWT authentication for user sessions
Protected pages, APIs, and conversation records
Input validation with request sanitisation
API rate limiting
CORS protection
Secure environment variables
HTTPS encryption
Secure MongoDB Atlas connections
Protected business knowledge
Controlled OpenAI API access
Safe error handling with backend logging

Architecture Diagram

How Conversations Changed

01
Users mainly depended on text-based chatbots
Users can communicate through voice or text
02
Language had to be selected manually
The system detects English, Hindi, Hinglish, and Roman Hindi automatically
03
Language changes could interrupt the conversation
Users can switch languages during the same conversation
04
Follow-up questions often lost earlier details
Previous messages help continue the conversation
05
Answers mainly depended on general AI knowledge
The system uses connected business information when needed
06
Users had to wait for the complete response
Answers appear on the screen in real time
07
Voice processing required separate tools
Voice input, AI response, and playback work in one flow
08
Earlier discussions were difficult to continue
Conversation history is saved across sessions

Results

For Users

01Users can communicate with the AI through voice or text in English, Hindi, and Hinglish. They can switch languages, ask follow-up questions, and continue earlier discussions without repeating the same details. Responses appear in real time, making conversations faster and easier to follow.

For the Client

01The client received a secure multilingual AI platform built for regular business use and future growth. The platform can support new integrations, more AI features, and more users as the business grows.

Business Impact

Teams often had to answer the same questions and check different sources to find the right information. Because of this, routine communication took more time. NovaMind AI made this process simpler and easier to manage. It improves response consistency and allows employees to focus on more important work instead of answering the same questions repeatedly. The platform also gives the business a practical way to use AI in daily operations and expand its use as business needs grow.

Business Impact

Around 85%Monthly Active Users

Business Impact

10,000+Total Conversations

Business Impact

Nearly 40% voice and 60% textVoice and Text Usage

Business Impact

Around 70% of conversationsHindi and Hinglish Usage

Business Impact

Nearly 55%Returning Users

Business Impact

Nearly 92%Voice-to-Text Success Rate

Business Impact

Around 97%AI Request Success Rate

Business Impact

2–3 secondsAverage Page-Load Time

Business Impact

300–500 millisecondsAverage API Response Time

Business Impact

Around 99.5%Platform Uptime

Visual Identity

NNovaMind AI

Inter

NovaMind AI

Color

#7C3AED
01Primary Purple124 58 237
#8B5CF6
02Bright Violet139 92 246
#050816
03Dark Background5 8 22
#0F172A
04Secondary Background15 23 42
#111827
05Card Background17 24 39
#FFFFFF
06White255 255 255
#E2E8F0
07Light Text226 232 240
#94A3B8
08Secondary Text148 163 184
#3B82F6
09Blue Accent59 130 246
#22C55E
10Success Green34 197 94
#EF4444
11Error Red239 68 68

Type

01 · Primary4 weights
Inter
ABCDEFGHIJKLMNOPQRSTUVWXYZ
abcdefghijklmnopqrstuvwxyz
0123456789
- Regular
- Medium
- Semibold
- Bold

Client Testimonial

We wanted an AI platform where users could speak or type in English, Hindi, and Hinglish without selecting a language each time. Team TISA understood this requirement clearly and developed NovaMind AI around the way our users communicate. Users can switch languages, use voice, and continue conversations without interruption. We are impressed with the final platform and satisfied with how the team managed the development process.

Albert Joe

Future Scalability

Future improvements can include:

01
Support for more Indian and international languages
02
Business-specific AI assistants
03
CRM and customer support integration
04
WhatsApp, Slack, and Microsoft Teams integration
05
Team accounts with role-based access
06
Advanced conversation analytics
07
Human support handover
08
Workflow automation

Lessons Learned

While building NovaMind AI, we learned that:

01
Multilingual AI needs more than translation
02
Hinglish and Roman Hindi need custom handling
03
Clear voice states and retry options improve usability
04
Useful context helps answer follow-up questions
05
Long chat history should be managed carefully
06
RAG works better with organised business data
07
Real-time streaming makes responses feel faster
08
Private chats need secure access
09
Performance metrics should be tracked from the start

Conclusion

NovaMind AI is a multilingual AI assistant created for smooth voice and text conversations in English, Hindi, and Hinglish. It understands language changes, remembers earlier messages, and gives useful answers using connected information. It provides the client with a secure system for customer support, virtual assistance and future AI features.