Some of the hardest problems businesses face today is not about ambition or ideas. They are about trust in data, speed of execution, and the ability to turn complexity into clarity. Teams want AI systems that respond instantly, explain their decisions, and improve continuously without breaking production. Yet behind the scenes, many organizations are slowed by fragile pipelines, disconnected tools, and systems that work in theory but fail under pressure. Chalk exists to close this gap, giving teams a data foundation strong enough to answer questions that truly matter.

Chalk is a data infrastructure company created for teams building serious machine learning and AI systems. Its platform provides the essential building blocks needed to design, deploy, and scale models that operate reliably in production. The name Chalk reflects the company’s mindset. Inspired by the chalkboard used by mathematicians, it represents precision, clarity, and the satisfaction of reaching a proven conclusion. The company’s visual identity draws from the QED symbol, a mark that signals a mathematical proof is complete. For Chalk, this is more than symbolism. It is a promise that AI systems should be explainable, auditable, and correct.

At its core, Chalk is a programmable data and feature platform designed to support low-latency inference, rapid iteration, and full visibility across the entire model lifecycle. It removes the friction that slows down AI teams and replaces it with a developer experience that feels natural, fast, and dependable.

Turning Data Chaos into Structured Intelligence

Modern AI systems consume data from everywhere. Web pages, images, text, transactions, logs, and user behavior all flow into models that must make decisions in milliseconds. Chalk helps organizations turn this messy, unstructured world into clean, structured, and auditable features that models can actually use.

The platform allows teams to build feature pipelines entirely in Python, without learning new languages or rewriting logic for production. Features are defined once and used everywhere, whether for training, testing, or live inference. Chalk handles the heavy lifting behind the scenes, managing joins, relationships, and execution plans automatically. This approach reduces errors, speeds up development, and ensures consistency across environments.

By unifying offline training and online inference, Chalk helps teams move from experimentation to production in days instead of months. Engineers and data scientists work from a single source of truth, confident that what works in development will behave the same way in real-world systems.

Real-Time Performance without Compromise

Speed is non-negotiable in modern AI. Chalk is built to serve fresh features at inference time with extremely low latency, enabling real-time decision-making without sacrificing accuracy. Its execution engine is optimized to deliver responses in just a few milliseconds, even under heavy load.

This performance allows organizations to combine lightweight logic upfront with deeper reasoning later in the workflow. Simple checks can happen instantly, while more complex AI reasoning runs only when needed. The result is systems that are both fast and intelligent, capable of catching edge cases others miss without slowing down the user experience.

Versioning, branching, and controlled rollouts are built directly into the platform. Teams can introduce new features gradually, test changes safely, and roll back instantly if needed. This makes AI development feel more like modern software engineering and far less like a risky experiment.

Making AI Transparent and Trustworthy

One of the biggest challenges in machine learning is visibility. As systems grow more complex, it becomes harder to understand how inputs turn into predictions. Chalk brings transparency to every layer of the AI stack, replacing black boxes with clear, traceable workflows.

Every feature can be traced back to its original data source, showing exactly how it was created and where it came from. End-to-end tracing makes it easier to debug pipelines, identify performance issues, and understand model behavior. Teams can monitor data drift, usage patterns, and system health in real time, ensuring models stay accurate as conditions change.

This level of observability builds confidence across organizations. Engineers trust the pipelines, data scientists trust the features, and business leaders trust the outcomes. Decisions become easier to defend, explain, and improve.

A Modern Approach to MLOps

Chalk simplifies MLOps by taking care of orchestration, caching, and feature serving at scale. Instead of stitching together multiple tools, teams work within a single platform that handles the full lifecycle of AI systems.

Features are discoverable and auditable across environments, making collaboration easier across teams. Experimental changes stay isolated until they are ready, and promotions to production follow controlled, repeatable processes. Models are versioned and managed with the same discipline as APIs, bringing consistency and safety to AI deployment.

With SDKs available across major programming languages, Chalk makes inference accessible to every part of the organization, whether powering internal tools or customer-facing applications.

Empowering Every AI Role

Chalk is designed to support the full spectrum of AI builders. AI engineers use it to create production-grade applications without wrestling with disconnected components. The platform supports prompt management, large-scale evaluations, embedding generation, and real-time context retrieval, all within a single workflow.

Data scientists benefit from a smooth path from notebooks to production. They can test new ideas, run experiments, and ship models without leaving familiar tools. Production features can be pulled directly into notebooks, and results can be traced all the way back to source data, ensuring experiments are grounded in reality.

By aligning the needs of engineers, scientists, and operations teams, Chalk removes silos and accelerates progress across the organization.

A Clear Path Forward for AI at Scale

As AI and advanced analytics continue to shape how businesses operate, the importance of strong data infrastructure cannot be overstated. Chalk provides the clarity, speed, and reliability needed to turn ambitious AI goals into working systems that deliver real value.

The company’s platform proves that AI does not have to be fragile or opaque. With the right foundation, it can be fast, transparent, and trustworthy. Chalk is building that foundation, one feature, one pipeline, and one proof at a time.

Marc Freed-Finnegan | Co-Founder & CEO

Marc spent many years at Google where he helped to launch the first version of Google Wallet. He went on to start Index, which Stripe acquired as its in-store payment solution, now called Stripe Terminal.

“Chalk’s data platform provides the essential building blocks for machine learning with an experience developers love.”