Build AI models the way you write software

Understand and debug your AI model with Silico.

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We've helped design AI for

The first platform for intentional model design

Frontier interpretability techniques

Goodfire's state-of-the-art methods for interpreting and improving models.

The model
neuroscientist

An agent that plans and runs experiments, returns results, and learns over time.

A model design environment

Your team's workspace for training and debugging models, built on our infrastructure for frontier scale.

What Silico makes possible

PREDICT

See inside every prediction.

Imagine thousands of model scientists reverse engineering your model. Decompose your model into interpretable features and see when predictions are driven by real understanding of biology, reasoning, or the physical world — or by spurious correlations and dataset artifacts.

CHECK HEALTH

Run a health check on your model.

Run comprehensive diagnostics on your model's internal representations. Identify problems like undertraining, information bottlenecks, feature collapse, and other pathologies before they impact downstream performance.

DEBUG

Debug model failures.

Precisely debug issues with model behavior, identify and remove confounders, and diagnose failures before they occur in production.

IMPROVE

Shape model behavior.

Once you understand what's driving predictions, you can do more than explain them. You can improve them. The same internals that make a model interpretable make it more capable: better sampling, better search, stronger predictors.

GENERALIZE

Generalize from less data.

Aggregate metrics hide where a model is actually failing. Target the specific learned structures driving behavior — and shift the training distribution, objective, or architecture to generalize further with the same or less data.

Silico works across all types of AI models

Learn more about how Siilco can help you understand, debug, and intentionally design your foundation model

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Training or fine-tuning an AI model? We partner with companies training foundation models across architectures and modalities to interpret their models. Contact us to learn more.