Concerned about Tecton? Watch now.

Real-time context for LLM inference

A unified interface to serve, evaluate, and optimize LLMs using structured and unstructured data with sub-5ms latency.

TALK TO AN ENGINEER
hero gradient image

Trusted by teams building the next generation of AI + ML

logologologologologo

LLM Toolchain benefits

# Use structured output to easily incorporate
# unstructured data in your ML pipelines
class AnalyzedReceiptStruct(BaseModel):
    expense_category: ExpenseCategoryEnum
    business_expense: bool
    loyalty_program: str
    return_policy: int


@features
class Transaction:
    # Named prompts enable editing prompts directly
    # in the Chalk UI without redeploying code
    llm: P.PromptResponse = P.run_prompt(
        "analyze_receipt_with_prompt_from_chalk_dashboard"
    )

    # Let Chalk manage injecting the
    # right features at inference time
    user_prompt: str = F.jinja(
        """
        Analyze the following receipt:
        Line items: {{Transaction.receipt.line_items}}
        Merchant: {{Transaction.merchant.name}}
        {{Transaction.merchant.description}}
        """
    )

Ship LLMs faster

Develop, evaluate, and deploy prompts and models in one system, with minimal glue code.

Built-in scheduling, streaming + caching

Inject live structured features directly into your prompts, without ETL or batch jobs.

Standardize LLM development

Use versioned, parameterized prompts and completions as first-class objects in your stack.

Scale without fragmentation

Unify feature engineering, vector search, LLM inference, and monitoring on a single platform.

LLM Toolchain Docs

One Platform. One Toolchain. All the way to production.

Prompt Engineering

Experiment with prompts on historical data using branches. Chalk tracks outputs, computes metrics, and promotes winning prompts with one command.

Model Inference

Deploy inference pipelines with autoscaling and GPU support. Write pre/post-processing in Python. Chalk handles the rest, including data logging and versioning.

Evaluations

Log and compare model outputs with quality metrics to pick the best prompt, embedding, or model—all versioned automatically in Chalk.

Embedding Functions

Use any embedding model with one line of code. Chalk handles batching, caching, and lets you safely test new models on all your data.

Vector Search

Run nearest-neighbor search directly in your feature pipeline. Use any feature as the query, and generate new features from search results.

Large File Support

Process and embed large files, docs, images, and videos at scale. Chalk handles batching, autoscaling, and execution with a fast Rust backend.

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

company logo

Connect your LLMs to the freshest data without ETL pipelines

@features
class ProductRec:
    user_id: Primary[User.id]
    user: User

    user_vector: Vector = embed(
        input=F.array_join(
            F.array_agg(
                _.user.products[
                    _.name,
                    _.type == "liked"
                ]
            ),
            delimiter=" || ",
        ),
        provider="vertexai",
        model="text-embedding-005",
    )

    similar_users: DataFrame[User] = has_many(
        lambda: ProductRec.user_vector.is_near(
            User.liked_products_vector
        )
    )
  • Retrieve structured features dynamically at inference time
  • Use Python (not DSLs) to define feature logic
  • Fetch real-time context windows with point-in-time correctness
  • Mix embeddings and features for fully grounded RAG workflows
CHALK FOR AI ENGINEERS

Design prompts like you design software

chalk_client.prompt_evaluation(
    evaluators=["exact_match"],
    reference_output="review.sentiment",
    prompts=[
        "analyze_sentiment-v1",
        "analyze_sentiment-v2",
        P.completion(
            model="gpt-5.1-2025-11-13",
            messages=[
                P.user_message(
                    """Analyze the sentiment of this product review:
Review text: {{review.text}}
Rating from this user: {{review.rating}}
Average rating for product:
{{review.product.average_rating}}
Average rating this user gives:
{{review.user.average_rating}}"""
                )
            ],
        ),
    ]
).to_pandas()
  • Write, version, and reuse prompts with structured parameters
  • Evaluate prompts and models using historical production data
  • Compare model performance on accuracy, latency, and token usage
  • Debug failures with end-to-end traceability and lineage
  • Deploy prompt + model bundles as artifacts with full observability
NAMED PROMPTS

The latest at Chalk

product

7 Common MLOps Challenges (and How to Solve Them)

2026.01.05

engineering

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

2025.06.02

customer story

Medely staffs critical healthcare in real-time with Chalk

2025.12.11

product

Quarterly Product Update: Fall

2025.11.10

See how Chalk compiles prompt logic, feature queries, and completions into optimized inference pipelines.

TALK TO AN ENGINEER

Explore more of Chalk‘s data platform

Temporal Aggregations

Temporal Aggregations

Define aggregations once. Reuse them across batch, online, and real-time workloads.

Training Data

Training Data

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

Feature Store

Feature Store

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

Real-Time Serving

Real-Time Serving

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