Chalk is an AI data platform built to deliver the right context to models and agents at the moment of inference. Modern ML and agentic systems are only as good as the data fed to them in real time, and Chalk focuses on the hard problem of computing, caching, and serving features and context with low latency and strong correctness guarantees.
Developers define their data logic as Python feature pipelines, and Chalk handles orchestration, online and offline serving, backfills, and freshness, so the same definitions power both training and production inference. This collapses the typical gap between data engineering and ML serving, where teams otherwise maintain separate batch and online systems that drift out of sync.
The platform targets latency-sensitive, data-intensive domains such as fintech, healthcare, and e-commerce, where decisions like fraud detection, credit underwriting, and personalization depend on combining fresh signals computed on the fly. As AI agents proliferate, Chalk positions the same infrastructure as the context layer that agents query to ground their actions in current, governed data.
In May 2025, Chalk raised a $50M Series A at a $500M valuation, led by Felicis with participation from Triatomic Capital and existing investors General Catalyst, Unusual Ventures, and Xfund. The San Francisco company is using the capital to expand its data and inference platform.
By unifying feature computation with low-latency serving, Chalk competes with feature stores and bespoke in-house data infrastructure while emphasizing developer ergonomics and real-time performance for production AI.