# Chalk > Chalk is the real-time data platform for AI and machine learning. It provides feature pipelines, low-latency serving, training data, and compute infrastructure in your cloud. ## Company - [Homepage](https://chalk.ai/): Chalk is the data platform for AI and machine learning. - [About Chalk](https://chalk.ai/about): Learn about Chalk, its mission, and its team. - [Careers at Chalk](https://chalk.ai/careers): Open roles and life at Chalk. ## Get started - [Book a demo](https://chalk.ai/book-demo): Talk with the Chalk team about your use case. ## Resources - [Blog](https://chalk.ai/blog): Technical articles, product news, and company updates. - [Customer stories](https://chalk.ai/customers): How teams build production AI and ML systems with Chalk. - [Events](https://chalk.ai/events): Upcoming and recorded Chalk events. - [Code examples](https://chalk.ai/code-examples): Examples of feature pipelines built with Chalk. - [An ML agent's investigation, in a Chalk Notebook](https://chalk.ai/try/agentic-ml-notebook): A recorded, read-only Chalk Notebook in which a coding agent investigates a default-rate rise, retrains a model on Chalk Model Training and tests it on a Chalk branch against production. - [Ship Better Models: A New ML Workflow with a Context Layer and Agents](https://chalk.ai/watch/use-chalk-notebooks-for-agents): Join us for a session on rebuilding the ML workflow around a real-time context layer and an AI agent that helps data teams investigate, iterate, and ship better models. - [Give Agents Context for Evals & Production](https://chalk.ai/watch/agent-context-evals): Join us for a session on building production-ready agents equipped with a context layer. Real-time context helps teams avoid the pitfalls of stale data: agents that misfire and unreliable evals. - [Life After Tecton Migrating to Chalk](https://chalk.ai/watch/databricks-tecton): Check out this on-demand webinar learn about your options following Tecton's acquisition by Databricks. - [How Chalk fuels Whatnot’s live shopping platform](https://chalk.ai/watch/whatnot-case-study): Chalk powers Whatnot’s real-time recommendation engine, enabling low-latency personalization and scalable ML infrastructure for the largest live shopping marketplace. ## Blog posts - [How Chalk Uses Chalk | How We Evaluate LLM Outputs Before We Trust Them](https://chalk.ai/blog/how-chalk-uses-chalk-evaluate-llm-outputs): How Chalk uses Chalk to evaluate LLM outputs before trusting them in production. - [Give agents exactly the access they need with Chalk MCP Gateway](https://chalk.ai/blog/chalk-mcp-gateway): Give agents exactly the access they need with Chalk MCP Gateway - [Chalk Model Gateway: Route AI in Your Own Cloud | Chalk](https://chalk.ai/blog/announcing-chalk-model-gateway): 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. - [The AI Conference 2026 Recap: What Agent Teams Asked | Chalk](https://chalk.ai/blog/ai-conference-2026-agents): 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. - [How Chalk Uses Chalk: Context Engineering for GTM | Chalk](https://chalk.ai/blog/how-chalk-uses-chalk-gtm-memory): 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. - [Making it easy to interface with Chalk’s query engine, with Chalk SQL and Chalk Dataframe](https://chalk.ai/blog/chalk-sql-and-chalk-dataframe): Learn how Chalk SQL and Chalk DataFrame let data and AI teams query, transform, and serve data across sources using familiar interfaces. - [How Chalk Uses Chalk: Building a Sandboxed Agent | Chalk](https://chalk.ai/blog/how-chalk-uses-chalk-mealbot): 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. - [Chalk Notebooks: where your ML agent does its work | Chalk](https://chalk.ai/blog/announcing-chalk-notebooks): 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. - [Agentic Machine Learning on Chalk: Introducing MCP Server and Chalk Assistant](https://chalk.ai/blog/announcing-chalk-mcp-server): 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. - [What is agentic machine learning? | Chalk](https://chalk.ai/blog/agentic-machine-learning): Agents now run the modeling loop end to end. What decides whether you can trust it is the data layer underneath. - [Context Has a Timestamp: Real-Time Agent Context with Chalk](https://chalk.ai/blog/context-has-a-timestamp): Why real-time, windowed context beats static retrieval for agents — illustrated with a poker example that becomes a fraud detector. - [What is a Context Engine? | Chalk](https://chalk.ai/blog/what-is-a-context-engine): 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 Feature Store?](https://chalk.ai/blog/what-is-a-feature-store): 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 Snowflake Summit Was Really About](https://chalk.ai/blog/snowflake-summit-agents-compute): What we heard at Snowflake Summit, and what we built to answer it. - [Announcing Chalk Compute: Time-Traveling Agent Sandboxes in Your Cloud](https://chalk.ai/blog/chalk-compute): Run agents against the exact data your production system would have served, in your cloud. - [Welcome Home, West Coast](https://chalk.ai/blog/sf-office-warming-party): Chalk celebrated the opening of its new San Francisco headquarters with 100+ engineers and builders from the Bay Area AI and ML infrastructure community. - [Chalk's Spring Fling: Where to Find Us This Season](https://chalk.ai/blog/spring-fling-schedule): 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. - [What Is Data Drift in Machine Learning | The Chalkboard](https://chalk.ai/blog/data-drift): 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. - [Why Your Feature Store Has a Freshness Ceiling | The Chalkboard](https://chalk.ai/blog/why-feature-stores-have-freshness-ceiling): 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. - [Quarterly Product Update: Winter](https://chalk.ai/blog/product-update-feb-2026): 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. - [Chalk x ODSC Meetup 2026](https://chalk.ai/blog/chalk-odsc-meetup-recap-2026): Recap from ODSC's first SF meetup of 2026, where we did a deep dive into real-time feature computation for marketplace recommender systems. - [7 Common MLOps Challenges](https://chalk.ai/blog/7-common-mlops-challenges): 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. - [Quarterly Product Update: Fall 25 | The Chalkboard](https://chalk.ai/blog/product-update-nov-2025): 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. - [Mission Lane Demo Recap](https://chalk.ai/blog/mission-lane-demo-recap): How Mission Lane manages thousands of ML features in production using Chalk. Demo recap covering feature reusability, live decisioning, and batch workflows. - [Fast Company Next Big Things in Tech](https://chalk.ai/blog/fast-company-2025): 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 in Your Cloud | The Chalkboard](https://chalk.ai/blog/deploy-in-your-cloud): 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. - [Chalk for Data Scientists](https://chalk.ai/blog/chalk-for-data-scientists): Your notebook code is production code with Chalk. Deploy directly from notebooks and experiment safely with branches. - [CEO Marc on The Information TV](https://chalk.ai/blog/marc-on-the-information-tv): 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. - [Chalk on TWiST500](https://chalk.ai/blog/chalk-on-twist500): 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 for Data Engineers | The Chalkboard](https://chalk.ai/blog/chalk-for-data-engineers): Data engineers waste weeks managing ML pipelines. Learn how to escape backfill nightmares and tool sprawl with declarative features on Chalk. - [SciPy 2025 Recap | The Chalkboard](https://chalk.ai/blog/scipy-2025-recap): 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. - [Quarterly Product Update: Summer | The Chalkboard](https://chalk.ai/blog/product-update-august-2025): This quarter, we delivered platform-level improvements that extend Chalk’s ability to support complex, production-grade ML systems. - [Verisoul Demo Recap | The Chalkboard](https://chalk.ai/blog/verisoul-demo-recap): 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. - [Feature store vs. Feature engine](https://chalk.ai/blog/feature-store-vs-feature-engine): 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. - [Chalk for AI Engineers](https://chalk.ai/blog/chalk-for-ai-engineers): Learn how AI engineers use Chalk to eliminate stale context, reduce tooling complexity, and ship AI features that work consistently in production. - [What is MLOps? Definition, Challenges, and Best Practices](https://chalk.ai/blog/what-is-mlops): What is MLOps? Learn the definition, meaning, and benefits of MLOps, plus key components, challenges, and best practices for deploying and maintaining ML models. - [Data Council 2025](https://chalk.ai/blog/data-council-2025): Insights from Data Council 2025 on the shift toward real-time inference, declarative data architectures, Python-first interfaces, and simplified data stacks—plus a look at the community shaping the future of data. - [Which LLM Wins at Nolan Trivia?](https://chalk.ai/blog/chalk-prompt-evaluation): We put Chalk’s native prompt evaluation framework to the test by benchmarking top models on a custom Christopher Nolan trivia dataset—showcasing how teams can evaluate and deploy LLM workflows with production-grade rigor. - [Chalk Raises $50M Series A to Power AI Inference](https://chalk.ai/blog/announcing-chalk-50-seriesa-funding): I'm incredibly excited to share that Chalk has raised a $50 million Series A at a $500 million valuation, led by Felicis, with participation from Triatomic Capital and our existing investors General Catalyst, Unusual Ventures, and Xfund. This funding marks a huge milestone and a powerful validation of our vision for real-time AI infrastructure. - [April Events Recap](https://chalk.ai/blog/events-recap-april-2025): Chalk's team recaps their April 2025 tour across major AI conferences, sharing what they learnt on real-time ML infrastructure and LLM integration. - [Quarterly Product Update: Spring](https://chalk.ai/blog/product-update-april-2025): This quarter’s updates help ML teams move faster in production with better performance, observability, and flexibility. - [Symbolic Python Interpreter](https://chalk.ai/blog/symbolic-python-interpreter): To eliminate Python’s performance bottlenecks in real-time ML, Chalk built a Symbolic Python Interpreter that converts flexible Python resolver code into optimized Velox-native expressions. This post dives into the technical journey and impact of enabling fast, parallel execution without sacrificing developer ergonomics. - [Quarterly Product Update: Winter](https://chalk.ai/blog/product-update-dec-2024): A roundup of Chalk's latest product releases, feature updates, and new documentation for the end of the year 2024. - [Quarterly Product Update: Fall](https://chalk.ai/blog/product-update-september-2024): A roundup of Fall 2024’s latest product releases, feature updates, and new documentation and tutorials. - [Chalk wins at GenAI Demo Day](https://chalk.ai/blog/genai-demo-day): Chalk and Elliot Marx won Best Technology at The GenAI Collective's Demo Day by showing how Chalk's feature store can be used to unify traditional machine learning features with generative AI for parsing unstructured data. - [Building PartiQL Support at Chalk](https://chalk.ai/blog/partiql): A deep dive into how we built support for PartiQL and DynamoDB on Chalk by using DuckDB's SQL parser. - [Quarterly Product Update: Summer](https://chalk.ai/blog/product-update-july-2024): New product releases and changelog updates from Chalk for summer 2024. - [Feature Store at Work](https://chalk.ai/blog/fraud-risk-case-study): Machine learning has long been deeply embedded in the field of fraud detection. A deep dive into how feature stores empower ML teams to build fraud models. - [Introducing Chalk NY](https://chalk.ai/blog/introducing-chalk-ny): Today we’re thrilled to announce Chalk's expansion with the opening of a New York City office. - [CB Insights AI 100](https://chalk.ai/blog/cb-ai-top-100-2024): Today we're proud to announce CB Insights recognized Chalk alongside Databricks in its 2024 AI 100 as one of the leading ML / AI Development Platforms globally. - [$10M Seed Round to Accelerate ML](https://chalk.ai/blog/announcing-chalk-10m-seed-funding): We’re proud to announce we’ve raised $10 million in seed funding led by General Catalyst, Unusual Ventures, and Xfund to build the data platform for machine learning and generative AI. - [Chalk Completes SOC 2 Type 1 Report](https://chalk.ai/blog/soc-2): Chalk is proud to announce the availability of our SOC 2 Type 1 Report. ## Capabilities - [Serve models on autoscaling GPUs in your own cloud](https://chalk.ai/capabilities/model-serving): Register a model from your training script and serve it on autoscaling GPUs in your own cloud. Call it from a feature pipeline, Chalk SQL, or its own endpoint. - [Isolated sandboxes for agent code, in your own cloud](https://chalk.ai/capabilities/sandboxes): Run agent-generated and untrusted code in gVisor-hardened sandboxes inside your cloud account, with default-deny egress, network policy and scoped identity. - [Train models on point-in-time correct data](https://chalk.ai/capabilities/model-training): Build training data as the world looked at each row's timestamp, from the definitions that serve production, then train and fine-tune on infrastructure you own. - [Accurate evals built on real data, for agents and models](https://chalk.ai/capabilities/agent-evals): Replay agents and models against production data as it actually was, with one knowledge cutoff on every tool call. Score with functions or LLM judges. - [Compute features at inference time, directly from the source](https://chalk.ai/capabilities/real-time-serving): Resolvers compute features at inference time, straight from your sources, in milliseconds. One definition for training and serving means no train-serve skew. - [One SQL engine to query your entire data estate](https://chalk.ai/capabilities/federated-queries): Join Postgres, Snowflake, BigQuery, APIs and streams in one SQL query. Chalk queries each source in place, at inference time or in bulk, without moving data. - [Python functions and autoscaling services, in your own cloud](https://chalk.ai/capabilities/functions): Deploy a Python callable with its resources and Chalk runs it on a schedule, event or queue with retries. Scaling groups turn it into an autoscaling service. - [Tracing, lineage and access control for production AI](https://chalk.ai/capabilities/governance-and-observability): Trace every query with its plan, timing and result, detect feature drift, follow lineage, and enforce access control down to a single feature. - [One governed endpoint in your cloud for every model your agents call](https://chalk.ai/capabilities/model-gateway): One governed endpoint in your cloud for frontier providers and your own open or custom models, with per-key budgets, model allowlists and automatic fallback. - [Every feature has a single source of truth](https://chalk.ai/capabilities/feature-store): Define each feature once. Chalk computes it on demand for training and inference, point-in-time correct with no train-serve skew, and serves it in under 5ms. - [Governed access to every MCP server your agents use](https://chalk.ai/capabilities/mcp-gateway): Register the MCP servers your agents can reach. Chalk injects credentials at call time, checks every call against your policy, and records a full audit trail. - [Notebooks that run against production data](https://chalk.ai/capabilities/notebooks): Chalk Notebooks run on a hosted kernel in your cloud and query the same deployment that serves your models. Test changes on a branch, with cached SQL results. - [Get AI and ML with context into production. Fast.](https://chalk.ai/capabilities/forward-deployed-engineers): Chalk's forward-deployed engineers are the people who built Chalk. They partner with your team to take critical AI workloads from architecture to production. - [Time traveling agent sandboxes](https://chalk.ai/capabilities/compute): Compute on Chalk - [Train models with production features](https://chalk.ai/capabilities/training-data): Generate consistent training datasets for ML. Chalk backfills data with point-in-time correctness, ensuring reproducibility and scalability. - [Temporal context at scale](https://chalk.ai/capabilities/temporal-aggregations): Generate consistent training datasets for ML. Chalk backfills data with point-in-time correctness, ensuring reproducibility and scalability. - [Real-time context for LLM inference](https://chalk.ai/capabilities/llm-toolchain): Chalk’s LLM toolchain unifies prompt engineering, embeddings, vector search, and real-time inference so you can ship LLMs faster, all the way to production. ## Customer stories - [How Grid accelerates model training and creates continuous improvement with Chalk](https://chalk.ai/customers/grid): See how Grid uses Chalk Model Training to cut model development time by over 50% and improve cash advance underwriting to approve more qualified members. - [Build or Buy? Why Melio Picked Chalk](https://chalk.ai/customers/melio): Why Melio replaced their in-house feature store with Chalk to power risk decisioning on every B2B payment and opened feature development to 20+ contributors. - [How Turo Built a Self-Serve ML Feature Platform for Search and Pricing with Chalk](https://chalk.ai/customers/turo): How Turo built a self-serve ML feature platform for search and ranking using Chalk’s compute-first feature store, reducing data overhead and enabling predictable, low-latency production performance. - [Medely staffs critical healthcare in real-time with Chalk](https://chalk.ai/customers/medely): Medely uses Chalk's self-serve feature platform to seamlessly deploy ML models, enabling real-time pricing and matching for healthcare staffing. - [Fintech Leader FIS Built a Real-Time Identity and Fraud Detection Platform Powered by Chalk](https://chalk.ai/customers/fis): Learn how FIS, a Fortune 500 payments leader, built a real-time fraud detection platform using Chalk. Compute hundreds of features in real-time, reduce fraud losses, and scale to billions of transactions. - [Powering Reliable, Explainable Data for Credit Decisioning at iwoca with Chalk](https://chalk.ai/customers/iwoca): Chalk powers iwoca’s credit decisioning infrastructure with precise, point-in-time feature computation. - [MoneyLion delivers AI-powered personal finance products with Chalk](https://chalk.ai/customers/moneylion): MoneyLion uses Chalk’s real-time data platform to unify ML development, accelerate feature delivery, and reduce time-to-production. - [How Chalk fuels Whatnot’s live shopping platform](https://chalk.ai/customers/whatnot): Chalk powers Whatnot’s real-time recommendation engine, enabling low-latency personalization and scalable ML infrastructure for the largest live shopping marketplace. - [Mission Lane’s Source of Truth for Credit Decisioning with Chalk](https://chalk.ai/customers/mission-lane): Mission Lane uses Chalk to power real-time credit approvals, fraud detection and customer-facing features, all from a single, consistent feature platform. As modeling and decisioning became core to the business, Chalk gave the Mission Lane team the infrastructure to move faster, reduce drift, and scale confidently across systems. - [How Apartment List uses Chalk to power personalized apartment recommendations](https://chalk.ai/customers/apartment-list): Apartment List uses Chalk’s real-time feature platform to power an individualized apartment search experience. - [Verisoul stops fake accounts with Chalk’s real‑time feature platform](https://chalk.ai/customers/verisoul): Discover how Chalk’s platform helped Verisoul increase detection accuracy with Chalk’s powerful, efficient, and secure platform. - [Vital predicts hospital wait times with Chalk’s feature platform](https://chalk.ai/customers/vital): Discover how Vital predicts hospital wait times with Chalk’s powerful, secure, and reliable feature platform. ## Deployments - [Run Chalk in your cloud](https://chalk.ai/deployments/self-hosted): Self-host Chalk's feature store on AWS, GCP, or Azure. Meet data residency and regulatory requirements without sacrificing real-time ML performance. - [Run Chalk as a fully managed platform](https://chalk.ai/deployments/chalk-hosted): Chalk-hosted feature store with enterprise-grade performance. Single-digit millisecond retrieval, built-in observability, and SOC 2 / ISO 27001 compliance. No infra required. ## Events - [Chalk at The AI Conference 2026](https://chalk.ai/events/AI-Conference-2026): Meet us in San Francisco and see how Chalk provides the data and infrastructure solutions to deliver real-time context to models and agents, train and fine-tune LLMs, and evaluate agents in production. From sub-5ms feature serving to an enterprise-grade agent runtime, build with the full AI/ML data platform for inference - entirely inside your cloud. - [Chalk at Fintech Devcon 2026](https://chalk.ai/events/fintech-devcon-2026): Meet us in Denver and see how Chalk powers real-time feature engineering to make AI real for fintech builders shipping models and agents. - [Chalk at AI Council 2026](https://chalk.ai/events/ai-council-2026): Meet us in San Francisco and see how Chalk powers real-time feature engineering to make AI real for businesses. - [Chalk at Snowflake Summit 2026](https://chalk.ai/events/snowflake-summit-2026): Meet us in San Francisco and see how Chalk powers real-time feature engineering to make AI real for businesses operating with the Snowflake AI Data Cloud. ## Landing pages - [The Platform for Production AI](https://chalk.ai/landing/ship-production-AI-faster) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/ai-data-platform) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/ai-conference-2026) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/fintech-devcon) - [Serve fresh, complex features sub 10ms, without streaming complexity](https://chalk.ai/landing/serve-features-sub-10-ms) - [The ML stack is broken where it matters most: production](https://chalk.ai/landing/ml-stack-is-broken) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/snowflake-summit) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/ai-council-one-page) - [More than a feature store, the data platform for AI + ML](https://chalk.ai/landing/data-platform-for-ai-ml) - [Trusted by leading ML teams for real-time feature engineering](https://chalk.ai/landing/trusted-by-ml-teams) - [AI and ML data, on demand.](https://chalk.ai/landing/ai-ml-data-on-demand) - [Real-time features for GenAI workflows](https://chalk.ai/landing/real-time-gen-ai) ## Solutions - [Real-time AI decisions on your Snowflake data.](https://chalk.ai/for/snowflake): Run real-time AI decisions on top of your Snowflake data with Chalk as a Snowflake Validated Connected Application. - [Chalk for Researchers](https://chalk.ai/for/researchers): Chalk for Researchers. - [Chalk for Data Engineers](https://chalk.ai/for/data-engineers): Chalk helps data engineers define, compute, and serve features in Python—ensuring consistency across batch, streaming, and real-time ML pipelines. - [Chalk for Startups](https://chalk.ai/startups): Build and scale AI/ML products with Chalk for Startups: real-time data infrastructure, hands-on engineering support, preferred pricing, and GTM amplification for early-stage teams. - [Chalk for AI Platform Engineers](https://chalk.ai/for/platform-engineers): Chalk gives platform engineers one end-to-end system to build, test, evaluate, deploy, observe, and improve AI systems. - [Chalk for MLOps](https://chalk.ai/for/mlops): Make ML production-grade with Chalk: reproducible, governed, and reliable across batch and real-time. - [Chalk for Data Scientists](https://chalk.ai/for/data-scientists): Empower data scientists to build reproducible, production-ready ML features. Chalk unifies training and inference for faster experimentation. - [Chalk for AI + ML Engineers](https://chalk.ai/for/ml-engineers): Power models with production-grade infrastructure, all from a single Python interface. Experiment faster, unify structured and unstructured data, serve features in real time with Chalk. ## Use cases - [Build agents with context to solve support tickets quickly.](https://chalk.ai/use-case/customer-support-agents): Build customer support agents on live account, transaction, and interaction data. Chalk serves fresh context at inference with governed access and millisecond latency. - [Build agents that operate with live context and help your team get work done.](https://chalk.ai/use-case/workflow-automation-agent): Build internal workflow agents that act on live business data with governed access and audit trails. Run agents in isolated sandboxes with fresh context and millisecond-latency queries. - [Trust every identity decision with real‑time fraud signals](https://chalk.ai/use-case/identity-fraud): Power real-time identity fraud detection with fresh, point-in-time correct ML features for risk scoring and abuse prevention. - [Power search and ranking with real-time features](https://chalk.ai/use-case/search-and-ranking): Build search and ranking models with real-time features, embeddings, and vector search. Re-rank results instantly with live context using Chalk. - [Price what is happening now.](https://chalk.ai/use-case/dynamic-pricing): Chalk serves pricing models fresh demand, supply, and behavioral features at inference-time. - [Build agents that manage abuse detection with real-time context.](https://chalk.ai/use-case/trust-and-safety-agents): Build agents that detect abuse, investigate threats, and take action using real-time user and platform context. Sandboxed execution inside your cloud. - [Power recommender systems with real-time features](https://chalk.ai/use-case/recommender-systems): Build real-time recommendation systems with fresh features, low latency inference, and no training-serving skew. Power dynamic personalization with Chalk. - [Build ML research agents with production context.](https://chalk.ai/use-case/ml-autoresearch-agent): Chalk gives agents the same context, production data, and feature definitions your models were trained on. Agents can research architectures, analyze model decisions, build features, evaluate results, and retrain models, creating a recursive self-improvement loop for the ML lifecycle. - [Real-time feature computation for payments](https://chalk.ai/use-case/payments): Build real-time payment decisioning systems with Chalk. Compute transaction risk features at authorization time with millisecond latency. - [Underwriting demands real-time data](https://chalk.ai/use-case/underwriting): Build real-time credit underwriting models with accurate, auditable data. Chalk powers credit decisions with point-in-time correctness. - [Build real-time ML systems that drive growth](https://chalk.ai/use-case/growth-decisioning): Build real-time ML decisioning systems for retention, pricing, and personalization using live customer data across the entire lifecycle. ## Workflows - [Ship agents that work.](https://chalk.ai/workflow/evaluate-agents): Replay agents against the state your systems actually served, score every run against the trace, and use the traces to make the model better. Prove the change is better before shipping. All in your cloud. - [Host AI models without building an inference platform from scratch](https://chalk.ai/workflow/open-model-serving): Run open-weight models like GLM-5.3, Deepseek v4 Pro, or Kimi K3 while keeping inference and data within your environment. Reduce the cost of model calls compared to running frontier closed state-of-the-art models and maintain data sovereignty. - [Turn your proprietary data into a model you own.](https://chalk.ai/workflow/fine-tune-models): Train and fine-tune open models on your proprietary data with Chalk. Build point-in-time-correct datasets, evaluate models side by side, and deploy the best model into production in your own cloud. - [Run agents safely.](https://chalk.ai/workflow/agent-sandboxes): Run agents and untrusted code in gVisor-isolated sandboxes inside your cloud. Deny by default, grant per workload, and give agents tools without giving them credentials.