The promise of real-time AI decision-making is being choked by cumbersome data pipelines, forcing data teams into a slow and inefficient tango between pre-computed warehouse data and production environments. This reliance on stale insights and fractured training-to-inference workflows is hampering the potential of AI to drive transformative business decisions.
The crux of the issue lies in the limitations of existing data tools, which are often designed for batch processing rather than the dynamic dance of live data. This forces data teams to pull pre-computed data from warehouses into production. This approach often leads to inherent delays and inaccurate answers. The resulting disconnect between training and inference pipelines further exacerbates the problem.
Enter Chalk.
Chalk, founded by Andrew Moreland, Elliot Marx and Marc Freed-Finnegan, is a data platform that powers ML and generative AI. Chalk’s developer experience enables data teams to declare features and their dependencies with idiomatic Python in online, streaming and batch environments. Chalk compiles these definitions into parallel pipelines that run on a Rust-based engine.
These pipelines use the exact same source code to serve temporally consistent training sets to data scientists and live feature values to models. This re-use ensures that feature values from online and offline contexts match and dramatically cuts development time.
With Chalk, engineers, data scientists and analysts can focus on their unique products while Chalk seamlessly handles data infrastructure.
“We deeply understand the pain points and workflows that make machine learning challenging, and we’ve applied those lessons here at Chalk," said Marx.
Moreland agrees and further said, “Existing tools in this space have painful and cumbersome developer experiences. Instead of relying on dated technologies like Spark, we built Chalk from the ground up to provide a seamless experience our users love.”
To accelerate development of its platform, reach new customers and grow its engineering and go-to-market teams, Chalk recently raised $10 million in seed funding led by General Catalyst, Unusual Ventures and Xfund.
Chalk’s data platform provides critical building blocks for real-time machine learning – a compute engine, an LLM toolchain, a feature store, integrated monitoring and branches for data science experimentation.
Incumbent technologies like Apache Spark and Databricks Runtime were designed for large, long-running jobs, not live traffic. However, fintech, e-commerce, healthcare, insurance and other mission critical applications require real-time decisions that incorporate fresh data.
For example, Ramp, the platform for modern finance teams, selected Chalk to power core lending and fraud models. Vital, an AI-driven digital health company, selected Chalk to power critical patient decisions.
“The opportunity to build a modern data platform that can be applied to many different industries is enormous and we see the benefits of backing experienced founders – especially ones we’ve backed before,” said Max Rimpel, Partner at General Catalyst. “We share a common vision to reshape the landscape of machine learning and data science and believe Chalk's team is well-equipped to achieve this.”
Edited by
Greg Tavarez
