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Chalk for AI + ML Engineers

Experiment faster, unify structured and unstructured data, serve features in real time, and power models with production-grade infrastructure, all from a single Python interface.

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Trusted by teams building the next generation of AI + ML

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Why AI/ML engineers choose Chalk

Unified data for every modality

Work with structured data like transactions and aggregations, and unstructured inputs like text, embeddings, and prompts

Faster experimentation loop

Experiment faster by defining features once in Python and reusing them across training and inference

Reliable, point-in-time datasets

Generate reliable training data with point-in-time correct backfills

Low-latency model serving

Serve models in real time with sub 5ms feature retrieval from heterogeneous sources

LLM-native feature workflows

Power LLMs with embeddings, vector search, and prompt construction

Reproducible by default

Reproduce and monitor all queries with built-in versioning and query logs

Chalk powers our LLM pipeline by turning complex inputs like HTML, URLs, and screenshots into structured, auditable features. We can serve lightweight heuristics up front and rich LLM reasoning deeper in the stack, catching threats others miss without compromising speed or precision.

Rahul Madduluri CTO

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LLM-ready infrastructure

@features
class Product:
    id: str
    description: str
    embedding: Vector = embed(
        input=lambda: Product.description,
        provider="vertexai",
        model="text-embedding-005",
    )

@features
class User:
    id: str
    purchases: DataFrame[Purchase]
    embedding: Vector = _.reviews[_.product.embedding].mean()
    recs: DataFrame[Product] = has_many(
        lambda: Product.embedding.is_near(
            User.embedding
        )
    )

LLM toolchain extends feature engineering into the world of GenAI

  • Embeddings support: define, store, and retrieve embeddings as features
  • Prompt construction: build structured, dynamic prompts using Chalk features
  • Vector search: retrieve semantically relevant context at query time

With Chalk, AI/ML engineers can combine structured and unstructured data for more accurate, context-rich LLM applications.

EXPLORE LLM TOOLCHAIN

Built for performance + scale

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Chalk’s compute engine, powered by Velox, delivers vectorized, low-latency execution across both batch and real time. Features are served in less than 5ms, even with complex transformations and joins.

REAL-TIME SERVING

From training to real‑time inference

@features
class Website:
    id: int
    content: str
    url: str
    completion: P.PromptResponse = P.completion(
        model="gpt-5.1-2025-11-13",
        messages=[P.message(
            role="user",
            content=F.jinja("""
            Analyze the following website:
            url: {{Website.url}}
            content: {{Website.content}}"""))
        ],
        # Use structured output as dataclasses
        output_structure=CompanyCompletion,
    )

Chalk unifies structured and unstructured data from the start of model development. This single definition can be:

  • Backfilled into training datasets
  • Served in real time at inference with millisecond latency
  • Versioned and reproduced at any point in time

Chalk ensures models are trained and deployed on the same feature logic, eliminating drift.

Explore how Chalk works

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Chalk for AI Engineers

2025.06.27

engineering

Which LLM Wins at Nolan Trivia? Chalk’s Prompt Evaluation in Production

2025.06.02

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Medely staffs critical healthcare in real-time with Chalk

2025.12.11

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Quarterly Product Update: Fall

2025.11.10

Ready to ship next‑gen AI/ML?

Talk to an engineer and see how Chalk helps 
AI/ML engineers deliver faster experimentation, real‑time inference, and LLM‑powered applications.

Talk to an Engineer