ML AUTORESEARCH AGENT
Build ML research agents
with production context.
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.
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Trusted by teams putting agents and models in production
Why Chalk
Parallelize exploratory investigations.
Automatically explore hypotheses - suggest an idea for a feature and have an agent automatically engineer features, backtest with point-in-time correct training sets, and push PRs. Concurrently run many investigations on burst-scalable infrastructure.
Automate the path from diagnosis to a tested fix.
Agents can build features, evaluate model changes, and prepare retraining runs using the same data and feature definitions that power production.
Less manual investigation required to improve your production models.
Your team sets the direction, and agents run the experiments. Each loop proposes a feature, backtests it on point-in-time correct training sets, and keeps only what improves the metric.
Parallelize exploratory investigations.
Automatically explore hypotheses - suggest an idea for a feature and have an agent automatically engineer features, backtest with point-in-time correct training sets, and push PRs. Concurrently run many investigations on burst-scalable infrastructure.
Automate the path from diagnosis to a tested fix.
Agents can build features, evaluate model changes, and prepare retraining runs using the same data and feature definitions that power production.
Less manual investigation required to improve your production models.
Your team sets the direction, and agents run the experiments. Each loop proposes a feature, backtests it on point-in-time correct training sets, and keeps only what improves the metric.
Increased model performance, faster
Turn production signals into model improvements without waiting for an engineer to start the investigation - helping your team iterate faster and improve outcomes at scale.
Power your team with agents you can trust
Ground every agent action in current feature data, lineage, and model artifacts. Get agentic recommendations based on the same production context your models use, and increase confidence in what agents find and propose.
Put engineering time where it matters
Reduce time spent on repetitive investigation work such as searching logs, querying data, and tracing feature issues with agentic feature development.
Build and ship features faster
Give agents access to production data, feature definitions, lineage, and model context. Ask what features could improve a model and where the underlying signals live. The agent investigates the data, proposes features, and helps turn them into production-ready pipelines.
Find the signals that improve predictions
Ask the agent which signals drive model behavior and where new predictive signals may exist. It analyzes feature values, distributions, and relationships across production data to surface promising signals. Move from hypothesis to validated feature faster.
Find what changed in production
When model performance drops, ask the agent why. Debug production models with the full context behind each decision.
Watch on demand: Give your ML team a research agent with production context
Models run on live features and real-time signals. Watch Chalk co-founder Elliot Marx build an agent with the same context. It can investigate model decisions, trace production issues, and identify changes that can improve model performance. Create an agentic loop from investigation to improvement grounded in the data your models use in production.
Automate your
model development workflows
Bring us the decision your team spent last week investigating. We'll show you how to build an agentic ML improvement cycle.
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