Production infra for
LLM inference
A unified interface to serve, observe, and optimize LLMs with structured and unstructured data — with sub-5ms latency, fallback, and real‑time eval.
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LLM Toolchain Benefits
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, videos—at scale. Chalk handles batching, autoscaling, and execution with a fast Rust backend.
Chalk powers our LLM pipeline, turning complex inputs—HTML, URLs, screenshots—into structured, auditable features. It lets us serve lightweight heuristics up front and rich LLM reasoning deeper in the stack, so we catch threats others miss without compromising speed or precision.
Real-time feature serving for LLMs
Connect your LLMs to the freshest data without ETL pipelines
- 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
Prompt engineering & evaluation
Design prompts like you design software
- 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