Business evolution
with AI & ML starts here

Coronatest.co provides COVID-19 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 could sell services for tests using Al.

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 customer system might not be able to get 100% accurate information.

1. The First Challenge was to get accurate results from different manufacturer’s strips.
2. The Second Challenge was to identify the perspective of a strip from the user’s camera feed.

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-19 positive or negative.

1. Resume builder is a unique system that is specifically created and managed for Medical professionals with a high-end backend with lots of customer records which will allow users to manage that essential data input from the backend.
2. NLP-based Resume Parser system which will parse the data from all the sections and feed the data to the relevant segment to manage its location.
3. Al and NLP Based Job match Score, Match the job description with your generated Resume and generate the best match score for your resume.

No such system will create custom-tailored resumes based on data specific to the medical/health industry.

It is a cutting-edge data science platform specifically designed for fishing companies based in Singapore. It leverages AWS SageMaker to provide fast training and easy deployment of a rain forecasting model. By accurately predicting whether it will rain tomorrow in specific regions, it helps fishing companies optimize their operations, minimize losses, and improve decision-making.

For fishing companies, weather conditions play a crucial role in determining the success of their fishing trips. Rainy weather can significantly impact fishing outcomes and lead to potential financial losses for the company. However, existing weather forecasting services often lack accuracy and fail to provide region-specific predictions tailored to the fishing industry's needs in Singapore.

It addresses this challenge by utilizing AWS SageMaker to develop a data science model for rain forecasting. The model is trained on historical weather data and other relevant factors specific to Singapore, such as local weather patterns, ocean currents, and atmospheric conditions. By leveraging machine learning algorithms and advanced analytics, it provides accurate predictions of whether it will rain tomorrow in various regions of Singapore. it offers a user-friendly interface where fishing companies can input their desired fishing locations and receive real-time rain forecasts. The platform also provides visual representations of the forecasted regions, highlighting the areas where rain is likely to occur. This empowers fishing companies to make informed decisions about whether to proceed with fishing operations or reschedule them based on the forecasted weather conditions.

The platform is hosted on AWS EC2 instances, ensuring high availability and scalability. AWS S3 serves as the storage solution for historical weather data, which is regularly updated to maintain the accuracy of the rain forecasting model. The entire infrastructure is managed using AWS services, enabling efficient data processing, model training, and real-time predictions.

The main challenges in the project revolve around achieving accurate rain forecasting for fishing companies in Singapore. These challenges include Data Quality and Availability: Ensuring the availability of reliable and up-to-date weather data for training the forecasting model. Region-Specific Predictions: Developing a model that can provide accurate rain forecasts at a regional level, catering to the specific fishing locations in Singapore. Real-Time Processing: Implementing a system that can process large volumes of data quickly and deliver real-time rain predictions to fishing companies. Feature Engineering: Identifying and incorporating relevant features from the historical weather data to enhance the accuracy of the rain forecasting model. Model Evaluation and Validation: Conducting rigorous testing and validation to ensure the model's performance and reliability in real-world scenarios. By addressing these challenges, it aims to revolutionize the way fishing companies in Singapore make weather-related decisions, minimizing losses, and improving their overall operational efficiency.

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.

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.

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.

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.

Homeez allows homeowners to plan their home design or redesign spatially. For that, first, they have to digitize the base floorplan of the homeowner. This was being done manually to digitize the floorplan automatically, there is no readymade software available.

• 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.

The main challenge was to create an Al engine that could do this task automatically with the scarcity of data.

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

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.

•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 vendors and customers for the enterprises to help them make decisions before entering into any business transactions. • We provide solutions to different platforms used by the party to the transaction to match the accounts on both sides with better accuracy and avoid future disagreements 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.

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

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.

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.

The main challenge is 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.

Super-Resolution Advanced RCAN-it is a cutting-edge Al-powered platform that revolutionizes the field of aerial image super-resolution. Our project offers a robust and efficient solution for enhancing low-resolution aerial images, enabling professionals in various industries to obtain high-quality, detailed images with ease. With our state-of-the-art model architecture and advanced techniques, we have overcome the limitations of traditional super-resolution methods and achieved remarkable results.

Obtaining high-resolution aerial images is crucial for applications such as remote sensing, surveillance, and environmental monitoring. However, capturing high-resolution images directly can be challenging and expensive. Existing super-resolution techniques often fail to produce satisfactory results for aerial images, especially when dealing with blur and low-resolution inputs. This creates a significant obstacle for professionals who rely on accurate and detailed aerial imagery for their work.

Our project, Super-Resolution Advanced RCAN-it, addresses the limitations of existing methods by introducing a modified and improved model architecture specifically designed for aerial image super-resolution. By enhancing the capability of the state-of-the-art super-resolution model, we have developed a solution that can handle blur images and generate robust high-resolution outputs. Our platform offers a user-friendly interface, allowing professionals to effortlessly upscale their aerial images by factors of 2X, 3X, and 4X. The model's enhanced performance is measured by an increase of 1.5 PSNR (Peak Signal-to-Noise Ratio) compared to the SOTA (State-of-the-Art) model. The output images exhibit improved clarity, sharpness, and detail, enabling professionals to extract valuable information and insights from their aerial imagery.

The main challenges in our project revolve around achieving accurate and robust super-resolution for aerial images, particularly when dealing with blur and low-resolution inputs. The model architecture had to be modified and improved to handle these challenges effectively. Additionally, ensuring real-time processing and maintaining a user-friendly interface were crucial aspects of our development process. Handling scenarios such as motion blur caused by aerial platform movement, atmospheric disturbances, and variations in lighting conditions required specialized techniques. Our team tackled these challenges through extensive experimentation, fine-tuning of hyperparameters, and leveraging advanced image processing methods. With Super-Resolution Advanced RCAN-it, professionals in fields such as urban planning, agriculture, disaster management, and infrastructure development can now obtain high-quality, detailed aerial images that support their decision-making processes and enable them to extract valuable insights from the data.

Our project does not rely on AWS services for deployment. Instead, we provide a locally deployable solution. The training process requires a folder containing low-resolution, blur-resolution, and corresponding high-resolution images. During testing, only the blur low-resolution images are needed as input, and the model outputs the corresponding high-resolution images.