Chalk

“Chalk has become a powerful addition to our machine learning infrastructure at Mission Lane. Chalk has enabled us to unify and streamline our feature calculations across both offline/batch-eval and online/live-decisioning use cases. We continue to be impressed by the flexibility and scalability of the system, and by the willingness of the Chalk team to work with us to get even more value out of it.”

“Chalk is a key component of our underwriting pipeline, increasing velocity across our engineering, risk, and data science teams. Chalk branches let us test new code against production pipelines without disrupting them, enabling us to quickly iterate on new features and enhancements without a PR. This allows us to offer unmatched capital products with more flexibility than other offerings in the market.”

“We’re applying AI and ML at scale across key areas of our energy business with Chalk’s feature platform. It enables high-performance computation over diverse data sources using clean, reusable code. The ability to mix Python and SQL gives our team the flexibility we need, while shared feature logic across projects improves consistency and accelerates development.”

“We don’t make millions of predictions a second. We make thousands of predictions of high consequence. Chalk underpins that work by giving us reliable, consistent data to power our models.”

“Chalk reduced the time it takes to create training sets and improved our confidence in feature consistency. The training data is correct, the features are consistent, and that reliability matters more than anything.”

“Instead of relying on stored data to stay in sync, we compute directly from the source. Chalk makes that both precise and reliable.”

“We no longer maintain an offline store. Chalk computes training datasets directly from the data itself, exactly when we need them.”

“Our data isn’t large, but every feature has to be precise. A single misaligned timestamp can change a lending decision. Chalk’s feature engine gives us conviction that every feature value is right.”

“It used to take 24 hours to regenerate a training set. Now it’s under an hour. We can trust every feature in it.”

“Adopting Chalk is the biggest singular win I have had as an ML engineer at this company.”

“The first product we ever deployed with Chalk paid for our team, probably more, in net revenue.”

“The features are just flowing so naturally ... I've never experienced anything like that, including my time at Spotify.”

“A solution like Chalk is profoundly important to our team because it provided the ability to buy engineering talent. I can rely on the fact that it's self-serve.”

“As our models get more complex, Chalk gets used more, not less.”

“For our high-funnel search workloads, predictability matters.”

“Chalk let us turn feature delivery into a self-serve workflow. We stopped waiting on other teams for every new feature and started shipping on our own cadence.”

“We had all the pieces of an ML platform, but no single, consistent way to productionize features.”

“Search, pricing, and risk all have very different requirements, but they all depend on features being predictable and available in production.”

“With Chalk, we can quickly add or remove a feature, test across all models, and then roll out quite quickly. For a team of nine that covers research and engineering for three different product lines, being able to iterate quickly and get improvements out and then move back to our roadmap was like the big shining light.”

“We probably write about half or a third of the code that we did with our previous solution — and get twice as much done. It’s a magical developer experience. The code is also much simpler. We’ve had developers from other teams come in and immediately understand stuff, which was 100% not the case before. Do more with less code.”

“We’re using Chalk to embed AI everywhere—from smart budgeting to fraud to lifecycle engagement. It’s foundational now.”

“Now teams contribute to features instead of requesting them. That changes everything.”

“Chalk lets DS build in Python and Pandas—which is familiar—and not worry about infrastructure. We can experiment and iterate much faster.”

“It’s easy to get started on Chalk, and the isolation model keeps everything safe by default. Teams don’t block each other anymore.”

“After switching to Chalk’s Query Planner, latency stabilized across peak load days. Even end-of-month Fridays, we stayed within SLA.”