This technology serves as an intelligent market research survey generator, designed to streamline the creation of survey questions by automatically generating them from a business overview, research objectives, and specific use cases. It further simplifies the process by converting Microsoft Word templates into SurveyJS (JSON) format, thus eliminating the manual coding typically associated with survey development. Additionally, the system offers auto-suggestions for subsequent surveys, leveraging insights gained from previous data. The value lies in its ability to significantly reduce the time and effort required to produce tailored research surveys. The list of global companies integrating this technology continues to grow including Sony, Red Bull, JBL, Disney, Spotify, ESPN, BCG, Paramount Pictures, and NASCAR. Potential applications extend to customer feedback collection, product development, and academic research, where efficient survey generation is crucial.

This technology serves as an advanced tool for integrated market research and analysis, tailored to empower Sales and Business Development teams. It allows these professionals to craft survey questions which, upon receiving responses, the technology extracts all pertinent themes and insights. It then generates a comprehensive summary, complete with citations, to encapsulate the overall sentiment and feedback from the survey participants. The platform is equipped with capabilities for contextual feedback, sentiment analysis, and concept testing, all of which contribute to delivering impactful analytics for expedited insights. The value lies in its ability to streamline the process of gathering and interpreting complex survey data, thus accelerating understanding and decision-making. Additionally, the technology has the potential to be applied in various other domains, such as customer experience management, product development, and market trend analysis, where understanding consumer perspectives is crucial.

This technology encompasses an advanced algorithmic stock trading system that leverages live US stock data fetched through web-sockets via an API, enabling prompt market analysis. It features a robust user-defined stock screening process that filters opportunities based on trading volume and technical analysis that integrates custom user strategies. Users receive timely email notifications. Additionally, the system includes a paper trading feature, simulating real-market conditions for strategy testing and refinement. It is important to note that the system does not make buy/sell recommendations independently. It enables the user to get notifications based on their customized strategy. Users must do their own due diligence before acting as past performance does not guarantee future performance. The value lies in its capability to facilitate a more informed and efficient stock trading experience. Potential applications extend to market trend prediction, portfolio management optimization, and educational tools for novice traders.

This technology employs federated learning, an approach where machine learning models are trained across multiple decentralized devices or servers with local data samples, to ensure sensitive data remains private and mitigate the risk of breaches, all while maintaining privacy and efficiency by not exchanging the data. It employs server-client authentication and advanced privacy techniques such as differential privacy or homomorphic encryption. Compatible across platforms and devices, the technology is anchored in a Kubernetes cluster-based setup with numerous worker nodes and a parent server, which has successfully trained a custom abdominal multiple organ segmentation model. The value proposition lies in its robust privacy-preserving capabilities coupled with its adaptability across various hardware environments. Additionally, this technology has the potential to enhance industries like healthcare, finance, and smart cities by providing secure, decentralized data analysis solutions.

This technology encompasses an automated email unsubscribe predictor designed to forecast which customers are likely to opt-out of email communications in the future. By integrating predictive analytics, the technology refines marketing strategies, allowing for the adjustment of campaign email lists to minimize unsubscribe rates. The value lies in enhancing customer retention and improving the efficiency of email marketing campaigns. Additionally, this technology has the potential to be adapted for use in predicting customer churn across various platforms, optimizing communication strategies, and personalizing user experiences.

This technology encompasses an advanced sound segmentation and classification system designed to process audio streams from video games. It utilizes a custom-developed sound classification model capable of identifying a variety of game sounds. The AI engine has been optimized to manage and analyze thousands of simultaneous streams, thereby enhancing the user's immersion by replicating the game's auditory environment with high fidelity. The value lies in its ability to create a deeply engaging and realistic gaming experience. Additionally, this technology has potential applications in virtual training environments and automated content moderation. It also has applications in security and manufacturing by isolating sound streams of interest.