BUILDING MACHINE LEARNING SOLUTIONS
We help organizations design, engineer, and deploy custom end-to-end ML solutions. Moreover, we provide add-on services like data gathering, data labeling, and software development to supplement ad-hoc business needs.
Our team specializes in understanding your specific domain problems and solve them using state-of-the-art machine learning models. We work with you to define custom evaluation metrics tailored to your business metrics.
Discovering data trends and patterns to derive real-time insights by leveraging expertise across industry sectors to solve business challenges
Enabling Jupiter to make meaningful product recommendations and predict future orders to enhance user experience and improve operational efficiency. Delivering end-to-end solutions from data processing, ML modeling, and deployment on their servers.
Helping Switch identify patterns among founding teams which result in successful ventures. Providing fully managed turn-key solutions from data collection, labeling, ML modeling, and maintenance of cloud infrastructure.
Managing ML-Ops for EVQLV by helping them scale custom and open-source ML models to create predictions on millions of biological sequences. Creating ML pipelines that combine data processing and ML predictions to generate meaningful business outcomes.
Helping Spotify develop a deep understanding of how personalised voice products and platforms influence user behavior. Building experimentation strategy, success metrics, and data products to guide the development of personalised user experiences via voice interfaces.
Meet The Team
Aarshay Jain pursued a master's degree in Data Science from Columbia University, New York. He followed it up with working as an ML Engineer at Spotify for 3 years. Aarshay has worked at an intersection of applied research and engineering while designing ML solutions to move product metrics in the required direction. He specializes in designing ML system architecture, developing offline models and deploying them in production for both batch and real time prediction use cases.
Keerti Agrawal pursued a Master's in Data Science from Columbia University, New York. She worked as a data scientist at Spotify. She brings experience and expertise in building time series forecasting models, experimentation strategies, and techniques to understand personalized user behavior. She is an IIT alum and has worked with a couple of Indian startups as well.