Saving Over $45M Annually Using AI for Fleet Management
Our Client, a Fortune 100 company with the world's 2nd largest vehicle fleet, sought operational efficiencies through state-of-the-art technologies. Coordinating over one hundred thousand vehicles, drivers, and operations in a complex ecosystem required real-time adaptability and intelligent decision-making. We developed an AI-powered smart fleet management system with real-time monitoring of the vehicle fleet. The platform provides tracking and analysis at the vehicle, area, and fleet levels, offering real-time analytics to fleet managers. By identifying fuel over-usage parameters using AI, timely interventions were made to reduce costs. Our fleet management solution empowered our customer to enhance operational safety and optimize its business, potentially saving over $45m annually.
Advancing MedTech Through Deep Learning
Our Client, a Multi-Billion Dollar Fortune 500 company with a global presence in over 70 countries, and a 100+ year-old legacy, in the MedTech industry. In a groundbreaking project, we collaborated with them to replicate research paper findings in a real-world setting, exploring the potential of a Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images. The challenge lay in spatially characterizing different tissues and identifying the correct bio-markers within histopathological images. Accuracy was crucial, as any mistake could have costly or even fatal consequences for patients. Through a diligent approach, we achieved an automated and precise replication, accelerating the diagnosis process and enabling early and accurate detection, ultimately saving lives.
Democratizing AI, Empowering Business via No-Code Platform
Our Client, a US-based startup, needed a no-code platform with highly flexible data connectors. The goal was to create a single drag-and-drop platform capable of ingesting data from diverse sources like SQL, Snowflake, and Cloud Storage Buckets. Our team built this no-code platform from the ground up and developed data connectors for various sources such as SQL, JSON, and Snowflake.
This includes a user-friendly web front-end with drag-and-drop functionality, as well as pre-processing and data cleansing recipes for commonly used data types. As a result, our client saved over 50% in development costs, increasing its competitiveness in the market.
Real-Time Contextual Advertisements Using AI
Our customer aimed to deliver contextual ads based on extracted data in video frames (Metadata). For instance, in a movie scene where the protagonist is eating breakfast, viewers could see a breakfast cereal ad. This required pre-processing of Media Streams and clustering Metadata for each video frame. We developed a proprietary Natural Language Processing algorithm to determine the scene context from metadata clusters. Based on this context, we retrieved relevant ads from a repository or shared the context with an advertisement publishing system (server) for serving contextual ads using shared keywords. This enabled real-time placement of new adds and replacement of embedded-existing ads, enhancing the viewer experience.
Efficient Accidental Claim Processing Using Computer Vision
Our customer, a leader and a unicorn in the digital Insurance space, wanted to increase efficiency of its claim processing by enhancing its damage detection and assessment process. We developed a Computer Vision aided solution for pre-insurance inspection to evaluate any pre-existing damage and for post-accident claims processing to evaluate the bodily damage, including both the parts and the type of damage (scratch, dent, complete destruction, etc.). This helped increase the accuracy of claim processing and significantly decrease the overall time to close.
Saving Critical Time in Healthcare Through OCR and NLP
Our customer, operating in the healthcare industry, faced the challenge of managing over 150 million patient healthcare records adding up to more than 1.5 billion pages. These paper-based records lacked searchability, often resulting in tedious manual search, potentially delaying critical decisions during emergencies. To address this, we designed a powerful solution by combining Optical Character Recognition (OCR) and NLP to digitize the entire set of health records. Leveraging AI, we made these records instantaneously searchable for doctors and other healthcare providers, thus saving valuable time in the critical heath related decision-making process.
Recommendation Engine Development for a Media Firm
Our Customer, operating in the Media Distribution Industry, ventured into the realm of short-form video apps, akin to TikTok. Initially, they adopted a commercial recommendation system from a renowned multi-billion dollar firm. However, the system's efficiency was disappointing, with a Normalized Discounted Cumulative Gain (NDCG) of just 0.03 – almost as ineffective as random selection. Seeking to boost their recommendation efficiency, they turned to us for AI model tuning. We successfully elevated the NDCG metric to 0.65, an improvement of over 20 times the original. This transformation proved pivotal, as improved recommendations directly led to increased platform engagement and higher ad revenues for our customer.
Cloud Data Engineering for an Innovative Diagnostic Product
Our customer aimed to develop an automated bench-top blood testing machine for lab-accurate results by integrating Hematology, Clinical Chemistry, and Immune Assay into one single run. We stepped in to optimize the process by developing an automated environment. This involved a comprehensive migration to the cloud, efficient data pipeline engineering, and data extraction from multiple systems. Additionally, we implemented an Azure IoT-based system for remote authentication and appliance management. The result? Our customer achieved the goal of revolutionizing healthcare testing, elevating diagnostic efficiency and accuracy.