# Chalk AI

**Source:** https://geo.sig.ai/brands/chalk-ai  
**Vertical:** Data Infrastructure  
**Subcategory:** Real-Time Feature Engineering  
**Tier:** Emerging  
**Website:** chalk.ai  
**Last Updated:** 2026-04-14

## Summary

Chalk is a feature engineering platform that allows ML teams to define real-time and batch features in Python and serve them with sub-millisecond latency for production inference.

## Company Overview

Chalk is a machine learning infrastructure company founded in 2021 by former Google and Stripe engineers, raising $26M to build a developer-friendly feature engineering platform. The platform allows data scientists and ML engineers to define features as Python functions that Chalk automatically computes, caches, and serves with millisecond latency for real-time model inference. Chalk handles the complexity of managing feature freshness across multiple data sources, computing features on-demand or through scheduled batch jobs, and maintaining consistency between training and serving environments. The company targets high-performance ML applications including fraud detection, credit decisioning, personalization, and real-time recommendation systems where feature latency directly impacts user experience and model effectiveness. Chalk's Python-native interface dramatically reduces the friction of building and maintaining real-time ML features compared to custom Flink or Spark streaming infrastructure. The company differentiates from Tecton and Feast through its emphasis on developer experience and the simplicity of its Python feature definition syntax that requires no distributed systems expertise to use.

## Frequently Asked Questions

### What is Chalk?
Chalk is a real-time feature engineering platform where ML teams define features as Python functions that Chalk automatically computes and serves with sub-millisecond latency for production ML inference.

### What problems does Chalk solve for ML teams?
Chalk eliminates the need to build custom streaming infrastructure for real-time features, ensures training-serving consistency, and provides automatic feature freshness management across multiple data sources.

### Who benefits from Chalk?
ML teams building real-time applications like fraud detection, credit decisioning, and personalization benefit most, as Chalk enables millisecond-latency feature serving without requiring expertise in distributed systems or stream processing.

### What problem does Chalk solve for ML teams?
Chalk eliminates the feature pipeline engineering burden that slows ML teams down — the work of building, maintaining, and serving the data transformations that convert raw data into ML-ready features, particularly when those features need to be available in real time with sub-millisecond latency at inference time.

### How does Chalk handle point-in-time correctness for training data?
Chalk generates training datasets that are point-in-time correct — meaning each training row uses the feature values that were available at the time the label was generated, not the current values. This prevents training-serving skew where a model is trained on future-leaking feature values that would not have been available at prediction time.

### What is Chalk's resolver model for feature definitions?
Chalk uses a Python-based resolver system where data scientists define feature transformations as typed Python functions, and Chalk automatically computes features using the appropriate resolver for each request context — whether that requires real-time computation, streaming aggregation, or offline batch processing.

### What data sources can Chalk connect to?
Chalk can connect to SQL databases, data warehouses, streaming sources, REST APIs, and other data sources, allowing feature definitions to pull from the full range of operational and analytical data sources that a company's models need to access.

### Where is Chalk AI headquartered?
Chalk is headquartered in San Francisco, California, and was founded by engineers with experience at leading technology companies building ML infrastructure at scale.

## Tags

startup, b2b, saas, ai-powered, data-warehouse, developer-tools, infrastructure, cloud-native

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*Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*