Watch on demand: Give Agents Context for Evals & Production

Give agents exactly the access they need with Chalk MCP Gateway

How Grid uses Chalk to accelerate model training and create continuous improvement

Chalk Model Gateway is an OpenAI-compatible model gateway that runs inside your existing Chalk deployment, in your own cloud. Fallbacks, budgets, rate limits, judge-based routing and tracing, with your provider keys and your model traffic never leaving your environment. No third-party gateway in the path, and no second service for your team to operate.

Run and route AI workloads in your own cloud with Chalk Model Gateway

At The AI Conference 2026, production AI teams asked how to keep agents accurate, cost-efficient, and governed under concurrent load. We shared what an agent stack needs to get there.

AIC26: What Agent Teams Asked Us

What it takes to give a model a usable picture of a few thousand accounts.

Chalk GTM Engineering built a working memory for every account Chalk has touched: defined, queryable fields a model can read instead of reconstructing thousands of histories. The same argument Chalk makes to customers, run on our own go-to-market data.

How Chalk Uses Chalk | A Working Memory for Our GTM

Learn how Chalk SQL and Chalk DataFrame let data and AI teams query, transform, and serve data across sources using familiar interfaces.

Making it easy to interface with Chalk’s query engine, with Chalk SQL and Chalk Dataframe

We built a Slack agent on Chalk Sandboxes that can reach Zerocater, Slack and OpenAI, and nothing else. Then we tried to make it leak. Network policies and auth injection held.

How Chalk Uses Chalk | MealBot

A workspace for agentic ML. Your agent investigates against the data your models actually see, and the notebook it leaves behind is the evidence you review before deciding what ships.

Introducing Chalk Notebooks: where your ML agent does its work

Chalk MCP Server connects AI agents directly to the Chalk primitives your team already uses and Chalk Assistant brings that agent inside the Chalk interface.

Introducing Chalk MCP Server and Chalk Assistant: Run Agentic Machine Learning on Chalk

Agents now run the modeling loop end to end. What decides whether you can trust it is the data layer underneath.

What is Agentic Machine Learning?

Why real-time, windowed context beats static retrieval for agents — illustrated with a poker example that becomes a fraud detector.

Context Has a Timestamp

Why agents fail on live decisions, and the system that fixes it.

An agent only knows what's in its prompt. A context engine computes the values that describe a customer's situation right now and assembles them at inference time, so the agent stops guessing at facts the business already has.

What is a Context Engine?

What Is a Feature Store in Machine Learning?

A feature store is more than a tool – it is a secret weapon that empowers teams to innovate faster, collaborate better, and deliver production grade machine learning.

What Is a Feature Store?

Build or Buy? Why Melio Picked Chalk

What we heard at Snowflake Summit, and what we built to answer it.

What Snowflake Summit Was Really About

Run agents against the exact data your production system would have served, in your cloud.

Introducing Chalk Compute: Time-Traveling Agent Sandboxes in Your Cloud

A hundred people, one new headquarters, and a lot of competitive mahjong. Chalk's San Francisco office is officially open.

Chalk celebrated the opening of its new San Francisco headquarters with 100+ engineers and builders from the Bay Area AI and ML infrastructure community.

Welcome Home, West Coast

We're celebrating our new office — and the timing couldn't be better. We've got a packed spring ahead, from Snowflake Summit to AI Council and more.

Chalk's Spring Fling is its most ambitious events season yet. From an SF office warming party to Bronze sponsorship at AI Council, a first-time presence at Snowflake Summit (booth #2611, June 1–4), and something in the works at Databricks Data & AI Summit, Chalk is showing up everywhere AI and data infrastructure is being built.

Chalk's Spring Fling: Where to Find Us This Season

Learn what data drift is in machine learning, how it differs from concept and model drift, and how teams detect and manage drift in production ML systems.

What Is Data Drift in Machine Learning (and How to Detect It)

Most feature stores are built around batch pipelines and materialized storage, which creates an inherent freshness ceiling. Tightening staleness settings cannot overcome architectural limits. This article explains why traditional feature stores struggle with real-time freshness and how a compute-first approach enables on-demand feature computation, lower latency, and improved correctness for time-sensitive ML use cases like fraud detection and underwriting.

Why Your Feature Store Has a Freshness Ceiling

How Turo Built a Self-Serve ML Feature Platform for Search and Pricing With Chalk

A roundup of Winter 2025’s latest Chalk product updates, covering performance improvements, new integrations, and infrastructure enhancements for running fast, reliable real time data pipelines in production.

Quarterly Product Update: Winter

Recap from ODSC's first SF meetup of 2026, where we did a deep dive into real-time feature computation for marketplace recommender systems.

Chalk x ODSC Meetup Recap 2026

MLOps enables teams to train and run models at scale. Learn why production ML is challenging, from feature inconsistency to fragmented data and how to solve it.

7 Common MLOps Challenges (and How to Solve Them)

Medely staffs critical healthcare in real-time with Chalk

This quarter, we expanded Chalk across three core dimensions: flexibility, visibility, and efficiency. Teams can now define and serve models alongside features, trace query behavior for diagnosis, and adapt feature logic dynamically without redeploying.

Quarterly Product Update: Fall

How Mission Lane manages thousands of ML features in production using Chalk. Demo recap covering feature reusability, live decisioning, and batch workflows. 

Mission Lane demo: Scaling ML with thousands of features

Powering Reliable, Explainable Data for Credit Decisioning at iwoca with Chalk

Chalk named to Fast Company's Next Big Things in Tech 2025 for helping AI teams get fresh data to their models in real-time.

Chalk Named to Fast Company's Next Big Things in Tech 2025

Chalk runs inside your VPC on Kubernetes so sensitive data stays resident, latency drops, and ML teams move faster. Hear the full story on the podcast.

Deploy where your data lives: Why Chalk runs in your cloud

Your notebook code is production code with Chalk. Deploy directly from notebooks and experiment safely with branches.

Chalk for Data Scientists

MoneyLion delivers AI-powered personal finance products with Chalk

CEO Marc on The Information TV unpacks why winning at AI isn't about bigger models but smarter infrastructure that keeps data and compute in your cloud.

CEO Marc on The Information TV

Our CEO Marc Freed-Finnegan joins This Week in Startups (TWiST) to explain why AI needs fresh data fast. Learn how Chalk eliminates the speed vs. freshness tradeoff.

Chalk on TWiST500

 Data engineers waste weeks managing ML pipelines. Learn how to escape backfill nightmares and tool sprawl with declarative features on Chalk.

Chalk for Data Engineers

How Chalk fuels Whatnot’s live shopping platform

The Chalk team went to SciPy 2025 in Tacoma and came back inspired. Here's our recap of all the advances we saw in Python's data ecosystem.

SciPy 2025 Recap

This quarter, we delivered platform-level improvements that extend Chalk’s ability to support complex, production-grade ML systems.

Quarterly Product Update: Summer

Verisoul shows how to catch sophisticated fraudsters by using Chalk to combine ML features with LLM intelligence. See how they rapidly iterated through three different techniques in under an hour.

Verisoul Demo Recap

Discover the critical differences between feature stores and feature engines. Learn when to use each approach and how feature engines enable real-time computation and rapid experimentation for ML teams.

◼ 404 - Not Found