GenAI summarization tool for the top management
In today’s world enterprises deal with a vast array of data, encompassing customer interactions, sales, marketing, finance, operations, and human resources. The challenge for top management is to distill this wealth of information into actionable insights – from straightforward historical data to complex comparative analyses and even future projections. Traditionally, synthesizing this data from various departments has been time-consuming, impacting the speed of decision-making.
To address this bottleneck, we’ve developed an AI-driven summarization tool. This tool allows management to input queries in simple text, similar to interacting with ChatGPT. Our GenAI Summarization tool then processes these queries, providing immediate, concise summaries and insights. This tool is designed to be operational and start delivering valuable summaries within four weeks of its implementation. Furthermore, it’s capable of ongoing learning and improvement, ensuring that it remains a highly effective tool for the organization’s needs.
AI-Based Stock Market Prediction
In this demonstration, we’ll showcase an AI model specifically trained to predict stock prices on a candlestick chart. Leveraging advanced transformer models, this tool can forecast the future prices of various stocks with an accuracy of up to 60 cents variation. This is particularly useful for day-to-day trading, providing traders with a valuable tool to inform their decisions. The model integrates live market data and offers forecasts for 1, 5, and 15 minutes into the future, maintaining the same level of precision in its price variation predictions.
AI based Medical Image Analysis
In our demonstration, we’ll present an AI model designed to analyze medical X-ray images. This advanced tool uses generative models to accurately identify fractures or irregularities in the images. It then compiles a detailed report based on its findings. With an impressive 90% accuracy rate, this tool is engineered to assist radiologists by providing them with reliable, AI-driven insights for their diagnoses.
Furthermore, the potential of this technology extends beyond X-rays. It can be integrated to analyze other types of medical imaging, such as MRI and CT scans. This versatility also allows doctors to use historical images as well, offering a comprehensive view of a patient’s medical history and aiding in more informed healthcare decisions.
Medical Report Generation
Here is the demonstration on AI technology designed for the healthcare sector. In this demonstration will highlight the AI’s ability to read, digitize, and summarize paper-based medical reports. Typically, when patients visit their doctors, they bring along medical reports in paper format. Doctors then face the daunting task of thoroughly reviewing these extensive historical documents. This process is not only time-consuming but also poses the risk of missing critical health information.
To revolutionize this aspect of patient care, we have developed an innovative AI model. This model transforms paper reports into digital format and then intelligently summarizes any noted abnormalities. This streamlined approach not only saves valuable time for healthcare professionals but also ensures that significant medical insights are accurately captured and highlighted. With this technology, we aim to enhance the efficiency and effectiveness of patient diagnosis and care.
AI Powered Interior Architect
This project successfully developed an automated interior design system powered by generative AI. It’s capable of generating interior design layouts for input images of rooms, based on user-provided prompts.
Key challenges faced during development included seamlessly integrating interior elements into existing room structures, accurately processing prompts to create suitable objects, and efficiently identifying room edges to maintain structural integrity.
Technical hurdles involved establishing real-time data processing for design prediction, minimizing inference time for design generation, and ensuring model adaptability to real-world scenarios through continuous learning.
The project offers a comprehensive solution in the form of an interactive web application and a seamlessly integrated desktop application. These platforms feature generative AI models capable of producing attractive and appropriate interior designs for various room types, eliminating the need for prompt engineering expertise. Users can simply specify the room category, and the AI system will autonomously generate the most suitable interior design
Intelligent Parcel Processing with AI
To streamline parcel processing and enhance efficiency, an automated system with the capacity to handle 500 parcels per hour was developed. This system leverages AI models for bill detection, text extraction, and entity recognition, effectively automating data entry and reducing labor costs.
Key challenges faced during development included accurate bill detection amidst color intensity variations in packaging and varying parcel orientations. Technical hurdles involved rotating detected bills to a standard orientation, establishing a real-time ETL pipeline, and optimizing inference time for maximum throughput.
The solution centers on an interactive desktop application equipped with AI models for bill detection, OCR-based text extraction, and NER for extracting crucial entities such as tracking IDs, shipping companies, GC numbers, and weight measurements. It also generates unique QR codes for each parcel.
The AI models are designed for continuous self-learning to adapt to real-world scenarios and maintain accuracy. The seamless integration of these models with the desktop application ensures high efficiency and accuracy.
GenAI Powered Legal Assistant
The project aimed to create a Generative AI module with a chat interface to assist legal teams. Challenges included developing a versatile knowledge base for all legal documents and handling structured document formats. Solutions involved vectorizing repeated document sections, prioritizing data security and anonymity, and implementing OCR for scanned images. The web application allowed users to upload various legal document formats and receive quick results through pre-built or custom prompts. The system aimed to provide answers within 1-2 seconds and analyze the document within 8-10 seconds, ensuring user privacy and efficient document processing.