Voltron Data
Claypot AI, now part of Voltron Data, specializes in unifying streaming and batch data for real-time machine learning applications, optimizing for latency, cost, and correctness.
Voltron Data and Claypot AI Integration
Voltron Data has recently integrated Claypot AI into its operations. Claypot AI is known for unifying streaming and batch data to optimize latency, cost, and correctness. This acquisition allows Voltron Data to enhance its capabilities in real-time machine learning applications, leveraging Claypot AI's expertise.
Claypot AI's Platform Capabilities
Claypot AI's platform is designed for real-time machine learning applications. It features a unified data abstraction that enables the use of any data source for rapid experimentation and deployment of new AI models. The platform is particularly effective in scenarios where low latency is critical, such as customer support, fraud detection, dynamic pricing, and personalization.
Target Use Cases for Claypot AI
Claypot AI focuses on improving business metrics and enabling new use cases while reducing computational and operational costs. Key use cases include real-time customer support, fraud detection, dynamic pricing, and personalization. The platform's ability to handle both streaming and batch data allows for optimized performance for each specific use case.
Remote Work and Culture at Claypot AI
Claypot AI operates with a flexible, remote-friendly culture, offering competitive compensation packages. The company hires remotely globally, provided the candidate is within ± 4 hours of PT time. Plans include bringing the team together a few times a year for in-person collaboration when safe. The company emphasizes transparency, collaboration, ownership, and learning, and provides opportunities for public speaking and career growth.
Claypot AI's Engineering Focus
Claypot AI maintains a high standard for engineering craftsmanship and is experienced in scaling data engineering and enhancing data science capabilities. Open roles include positions for founding engineers in machine learning and infrastructure, particularly in San Francisco, CA. The company's experts have significant experience in both distributed systems and machine learning, creating a robust community of over 15,000 ML practitioners.