Investing in Chalk

While much attention has been focused on increasingly sophisticated AI models, the real challenge for developers lies in working with data at inference time. Enterprises need a way to rapidly iterate with data to see what value they can generate with LLMs.

This act of using LLMs to make real-time decisions, which depend on instant access to high-quality, current data, presents several hurdles stemming from traditional data stacks:

1. Real-time data processing: Modern AI applications require fresh data to make accurate predictions, but integrating disparate data sources in real-time is complex.
2. Maintaining low latency: For many applications, predictions must be delivered in milliseconds, not seconds or minutes, to avoid the decision-making delays inherent in batch processing.
3. Computational complexity: As models become more sophisticated, the computational requirements for inference grow exponentially.
4. Development-to-production gap: Data scientists build models in controlled environments, but deploying them in production requires an entirely different set of tools and expertise.

These challenges create a significant barrier for organizations looking to leverage AI effectively. Thankfully, Chalk is here to build the data platform for inference. This means that they can have a market that’s bigger than current leaders like Databricks.

Chalk’s platform enables data teams to build and deploy machine learning with unprecedented efficiency and reliability. At its core, Chalk streamlines data processing, offering critical functionalities such as:

• Real-time feature engine: This allows developers to create pipelines that execute models end-to-end, optimized in real-time.
• Seamless deployment: The platform bridges the gap between model training and production deployment, eliminating traditional friction points.
• Comprehensive testing and debugging: They have robust tools for performing unit tests, caching features, and eliminating data errors.
• Ultra-low latency: Their architecture enables computationally complex inference while maintaining 5-millisecond data pipelines at massive scale, which is necessary for real-time applications.

Behind Chalk's impressive offering is an exceptional founding team with a proven track record of building and scaling successful technology companies. Marc founded Index, which was acquired by Stripe, and he brings deep expertise in building and growing developer-focused platforms. Elliot and Andy co-founded Haven Money, which was acquired by Credit Karma, and both bring extensive experience in data infrastructure.

We're thrilled to lead Chalk's Series A round. As compute shifts from training to inference across the industry, Chalk is perfectly positioned to capitalize on this transition.

The AI infrastructure market is growing rapidly, and Chalk’s ability to handle computationally complex inference while maintaining ultra-low latency addresses a critical need in the market. As companies increasingly move from experimenting with AI to deploying it at scale, the demand for a robust, efficient data inference platform will only grow. Chalk is already gaining traction with impressive technical teams from companies like Whatnot, Doppel, Melio, and more.

Chalk is becoming a foundational layer in the AI stack, enabling organizations across industries to bridge the gap between AI potential and real-world impact. We're excited to partner with Marc, Elliot, Andy, and the entire Chalk team on this journey.

‍