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Catalyst is a platform for building and deploying task-specific AI models. Instead of relying on large general-purpose models for every task, Catalyst helps you collect production data, evaluate model quality, fine-tune smaller models optimized for your workload, and deploy them on dedicated infrastructure. The platform also provides access to open-source and Inference.net-trained models (like Schematron for structured data extraction) through an OpenAI-compatible API.
Why task-specific models? Smaller models fine-tuned on your data are faster, cheaper, and often more accurate than frontier models for well-defined tasks. Catalyst gives you the tools to get there, from capturing production data to deploying the trained model.

📍 TODO:MEDIA

Hero visual showing the Catalyst improvement loop (Observe → Evaluate → Train → Deploy). Could be a screenshot of the dashboard, animated diagram, or short video/gif.

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How it works

  1. Observe - Route your LLM traffic through Catalyst’s gateway to capture every request, response, cost, and latency metric. Keep using any provider or model.
  2. Evaluate - Define rubrics that describe what “good” looks like for your use case, then score model outputs systematically across candidates.
  3. Train - Fine-tune a task-specific model on your production data. The platform handles base model selection, training parameters, and compute.
  4. Deploy - Ship the trained model to a dedicated GPU with an OpenAI-compatible API. One line of code to switch over.
Not every team goes through all four steps. Many start with observability and evals alone. The platform is useful at every stage.

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Pick your starting point

Record your first LLM call

Route traffic through the Catalyst gateway to automatically trace LLM calls and view metrics.

Run your first eval

Define quality, measure it, and compare models side by side.

Train and deploy a model

The full loop: data, training, and a production endpoint.

Use the Inference API

Access open-source and Inference.net models directly.