Chalk, the data platform for AI inference, announced today that it has raised a $50 million Series A at a $500 million valuation. The round was led by Felicis with participation from Triatomic Capital and existing investors General Catalyst, Unusual Ventures, and Xfund. Aydin Senkut, Founder and Managing Partner at Felicis, will join Chalk’s board. The capital will be used to accelerate development of Chalk’s platform, onboard new customers, and grow its engineering and go-to-market hubs in San Francisco and New York.
As AI adoption accelerates, compute is shifting from training to inference to improve predictions, transform customer experiences, and reduce costs. Existing solutions like Databricks and Snowflake solve training data pipelines, and feature stores provide low-latency access to pre-computed data. But these incumbents don’t provide a solution for applications that require fresh data, with complex computation, at inference time.
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Chalk fills a critical gap in the market – inference data pipelines. Chalk’s real-time data platform enables customers to make predictions with fresh data at inference time to prevent identity theft, issue instant l****, increase clean energy efficiency, and moderate harmful content.
Senkut shared, “Chalk is poised to become the Databricks of the AI era. It’s one of the fastest-growing data companies we’ve ever seen. The team has fundamentally redefined how data moves through the AI stack, a crucial advancement for chain-of-reasoning models. What’s even more remarkable is Chalk’s ability to deliver 5-millisecond data pipelines at massive scale – something that, until now, was considered out of reach. We couldn’t be more excited to partner with Marc, Elliot, and Andy, who are all repeat technical founders passionate about building infrastructure that delivers an incredible developer experience.”
Marc Freed-Finnegan, Chalk Co-Founder and CEO, added, “We feel incredibly fortunate to have Aydin and Felicis as our partners for the next phase of our growth. We have a shared vision of the future, and we’re honored to be part of the cohort of companies they have invested in.”
Chalk powers real-time ML across industries including fintech, identity, healthcare, and e-commerce. Companies like Socure, Found, Medely, and Iwoca use Chalk as a core infrastructure layer across their business.
“Chalk helps us deliver financial products that are more responsive, more personalized, and more secure for millions of users. It’s a direct line from infrastructure to impact,” said Meng Xin Loh, Senior Technical Product Manager, MoneyLion.
Chalk has become critical infrastructure for its customers by enabling teams to rapidly operationalize machine learning and AI. At its core, Chalk’s Compute Engine empowers teams to write features in pure Python, automatically translating them into high-performance C++ and Rust pipelines to deliver real-time data without complex ETL. Additionally, Chalk’s LLM Toolchain unifies structured and unstructured data, offering native vector storage, automated evaluations, and seamless integrations with major LLM providers.
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Rahul Madduluri, CTO at Doppel, said, “Chalk powers our LLM pipeline, turning complex inputs — HTML, URLs, screenshots — into structured, auditable features. It lets us serve lightweight heuristics up front and rich LLM reasoning deeper in the stack, so we detect threats others miss without compromising speed or precision.”
Chalk was co-founded by Freed-Finnegan, Elliot Marx, and Andrew Moreland — veterans of fintech and data infrastructure. After meeting at Stanford, Marx and Moreland solved large-scale data problems at Affirm and Palantir before co-founding Haven Money, acquired by Credit Karma. Before Chalk, Freed-Finnegan helped launch Google Wallet and started Index, acquired by Stripe (it’s now called Stripe Terminal). Across these ventures, the team saw how real-time data pipelines enabled entirely new product categories and business models. Fast forward to today — real-time decisions at inference are essential for all modern applications, and Chalk makes that possible.
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