Temporal context at scale

Define aggregations once and reuse them across batch, online, and real time.

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Trusted by teams building next generation of AI + ML

Alway-fresh features

Continuously update aggregations from streams or compute at the leading edge of your transactional sources for maximum freshness.

Consistent across training and serving

Define aggregations once and reuse across batch, online, and real time without drift.

Efficient at scale

Incremental updates keep queries fast and memory predictable as data volume grows.

Join across streams

Aggregate across streams, warehouses, and databases through our feature engine.

Alway-fresh features

Continuously update aggregations from streams or compute at the leading edge of your transactional sources for maximum freshness.

Consistent across training and serving

Define aggregations once and reuse across batch, online, and real time without drift.

Efficient at scale

Incremental updates keep queries fast and memory predictable as data volume grows.

Join across streams

Aggregate across streams, warehouses, and databases through our feature engine.

Alway-fresh features

Continuously update aggregations from streams or compute at the leading edge of your transactional sources for maximum freshness.

Consistent across training and serving

Define aggregations once and reuse across batch, online, and real time without drift.

Efficient at scale

Incremental updates keep queries fast and memory predictable as data volume grows.

Join across streams

Aggregate across streams, warehouses, and databases through our feature engine.

Chalk’s performance directly affects the quality of our search and discovery models, which power everything from price flexibility to apartment ranking. The ability to call real-time features without dealing with stream complexity has been huge for us.

Matt Weale Software engineer

Materialized aggregations

Precompute incremental buckets that update as events arrive for fast, production-ready features.

Cache and materialize features

Windowed aggregations

Run exact queries over any time horizon to rescan historical data.

Define features over time ranges

Streaming + materialized aggregations

Apply materialization directly to live event streams. Features update continuously without needing to store raw data.

Aggregate functions on streams

How temporal aggregations work

Streaming + materialized aggregations

Apply materialization directly to live event streams. Features update continuously without needing to store raw data.

Aggregate functions on streams

How temporal aggregations work

Materialized aggregations

Precompute incremental buckets that update as events arrive for fast, production-ready features.

Cache and materialize features

Windowed aggregations

Run exact queries over any time horizon to rescan historical data.

Define features over time ranges

Streaming + materialized aggregations

Apply materialization directly to live event streams. Features update continuously without needing to store raw data.

Aggregate functions on streams

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Temporal context for your ML features

Talk to an engineer and see how Chalk can  power your production AI and ML systems.

Talk to an engineer

Explore more of Chalk’s data platform

LLM Toolchain

Unify prompt engineering, embeddings, vector search, and inference to ship LLMs faster.

Training Data

Generate point-in-time correct training datasets from production feature definitions.

Feature Store

Define features once, and compute them on demand for training, batch scoring, and inference.

Real-Time Serving

Serve production features at inference time with single-digit millisecond latency.

LLM Toolchain

Unify prompt engineering, embeddings, vector search, and inference to ship LLMs faster.

Training Data

Generate point-in-time correct training datasets from production feature definitions.

Feature Store

Define features once, and compute them on demand for training, batch scoring, and inference.

Real-Time Serving

Serve production features at inference time with single-digit millisecond latency.

LLM Toolchain

Unify prompt engineering, embeddings, vector search, and inference to ship LLMs faster.

Training Data

Generate point-in-time correct training datasets from production feature definitions.

Feature Store

Define features once, and compute them on demand for training, batch scoring, and inference.

Real-Time Serving

Serve production features at inference time with single-digit millisecond latency.