Neuramonks

Neuramonks built a Real-Time AI Roleplay Agent Platform that enables realistic, voice-based sales training. In this system, an AI agent acts as a customer while a human participant plays the salesperson. Conversations happen live using low-latency voice communication, closely simulating real sales calls.

After each session, the platform automatically generates structured performance scores and actionable feedback to help sales teams improve. The platform combines a custom frontend, WebRTC-based real-time audio, LLM-powered roleplay intelligence, and automated evaluation workflows. This allows org

The Challenge
Traditional sales roleplay training is costly, hard to scale, and often inconsistent. It depends on human roleplay partners, provides subjective feedback, and relies on scripted scenarios that fail to reflect real customer behavior. As a result, sales teams struggle to prepare for real-world conversations.

The Solution
We developed a real-time AI roleplay system where the AI acts as a dynamic, context-aware customer during live voice conversations. Sales representatives engage naturally, while the AI responds with realistic questions, objections, and follow-ups. After each session, the system analyzes performance and delivers category-wise scores covering communication, objection handling, clarity, and overall sales effectiveness. Roleplay agents can be customized by industry, persona, and training goals.

Key Features & USP
Key highlights include real-time voice roleplay via WebRTC, realistic customer behavior powered by OpenAI Realtime models, and unbiased performance scoring with detailed feedback reports. The platform is built for scalability and easy adaptation across industries.

Tech Stack:

Frontend: React
Backend: Node.js, Python, FastAPI
Real-Time Communication: LiveKit
AI & Evaluation: OpenAI Realtime LLMs, Dify workflows

USP:

Homeez is a platform that connects homeowners directly to suppliers in their entire renovation process. Homeez uses the latest technology to offer an end-to-end digitalized solution - beginning from spatial planning using DIY tools like Design Now, all the way to managing the process using our unique Time Schedule tool

PROBLEM STATEMENT:

Homeez allows homeowners to plan the home design or redesign spatially. For that, first, they have to digitize the base floorplan of the homeowner. This was being done manually 

SOLUTION:

• We created an Al engine that can make the digital version of the floorplan automatically.
• We also created a scalable architecture to process multiple requests simultaneously.

TECHNOLOGY:

• Python
• Keras/Tensorflow
• OpenCV

INFRASTRUCTURE

• AWS lambda
• AWS ec2
• AWS S3

USP:

Brandspot is a platform where you can experience seamless and precise background removal like never before with our cutting-edge Al technology. Achieve pixel-perfect results in seconds, effortlessly separating subjects from their backgrounds. Our user-friendly interface and real-time processing empower you to remove backgrounds from images with ease. Our Al project offers the ultimate solution for professionals, businesses, and creative enthusiasts.

PROBLEM STATEMENT:

Removing backgrounds from images is a time-consuming and tedious task, that requires manual effort and expertise. Existing methods often lack accuracy and efficiency, leading to subpar results. This creates a significant bottleneck for professionals, businesses, and creative individuals who rely on high-quality background removal. It's hard to achieve a user-friendly interface, real-time processing, and unparalleled precision all on one platform.

SOLUTION:

Our project aims to address this challenge by leveraging state-of-the-art Al technology to automate and streamline the background removal process. With our user-friendly interface and compatibility across various media formats, our solution simplifies background removal, empowering professionals and creatives to effortlessly enhance their

TECHNOLOGY

• Python
• Django-Gunicorn-nginx
• Pytorch
• Opencv

INFRASTRUCTURE

• AWS S3
• AWS ec2

CHALLENGES

The main challenge lies in achieving accurate and robust background removal in complex scenarios while ensuring real-time processing.
Handling scenarios such as fine hair details, fuzzy edges, overlapping objects etc.

USP:

Caresync is a Al bot that enables users to track their medicine orders in real-time and collect feedback from users to identify areas of improment. The bot will provide order updates, respond to user queries. and facilitate seamless communication between users and the pharmacy.

PROBLEM STATEMENT:

The current process of tracking medicine orders and gathering user feedback is manual and Inefficient, resulting in delays, poor customer experience, and missed opportunities for improvement. There is a need for an automated bot that offers real-time order tracking updates and enables seamless user feedback collection to enhance customer satisfaction, streamline operations, and identify areas for enhancement in the medicine ordering process through a convenient and secure platform like WhatsApp.

SOLUTION:

The CareSync project will develop a WhatsApp bot that offers real-time order tracking, seamless user Interaction, and automated feedback collection. By integrating with the order management system and customer support, the bot will provide timely updates, address user queries, and gather valuable

TECHNOLOGY:

• Python
• Flask

INFRASTRUCTURE:

Meta Business Platform

CHALLENGES:

The main challenge in integrating the WhatsApp bot with existing systems, developing accurate natural language processing capabilities, ensuring scalability and high performance, and promoting user adoption and engagement. Additionally, maintaining data privacy and security throughout the process will be crucial to protect user Information and comply with regulations. Another challenge was to handle delays or irregular user conversations and maintain a smooth user experience.ew

USP:

Extensively, we are introducing Al-based solutions for accountants that are capable of solving laborious works with help of RPA (Robotic Process Automation) technology.

PROBLEM STATEMENT:

Current world of Accounts and Audit reporting so many manual processes are repetitive and many hidden layers of complexity will arise. Because of this accounting is considered to be a lengthy and also a false calculating problem because of human errors.

SOLUTION:

Extensive goals associated with Balance confirmation & Reconciliation, and Hyper ledgers.

It is developed with a high-tech approach and saves a lot of your time spent on training and updating your staff and systems.

To provide a 360-degree view of vendor and customer for the enterprises to help them to make decisions before entering into any business transactions.

We provide solutions to different platforms used by the party to the transaction and match the accounts on both the side with better accuracy and avoid future disagreement by way of resolving on a real-time basis. Also ensuring financial and legal compliance in transactions over there and eliminating insecurity in transaction manipulation.

TECHNOLOGY:

• Laravel
• Python
• Tensorflow
• MongoDB
• BERT, Spacy
• Tensorflow

INFRASTRUCTURE

• AWS 53
• S3

USP

Coronatest.co provides covid kits to airlines to test for Covid-19 in the passengers. They wanted to create a system where authentic tests with result proofs were stored in a secure cloud. Now, the company wanted to create a reseller dashboard so that they can sell services for tests using Al.

PROBLEM STATEMENT

Purchased test strips will be used by the customer but because of the system's loophole, people are able to manipulate their test result by sending a false test, and this way custorger system might not be able to get 100% accurate information.

SOLUTION

We have created multiple ML models to solve different tasks like identification of covid strip, detecting the location of covid results in different types of strips, and accurately detecting if the person is covid positive or negative.

TECHNOLOGY

• Python
• Tensorflow
• MongoDB

INFRASTRUCTURE

• Google cloud

CHALLENGES

• The First Challenge was to get accurate results from different manufacturer's strips.
• The Second Challenge was to identify the perspective of a strip from the user's camera food.