docs.goodfire.ai
Understand and debug your AI model
There is remarkable mathematical structure and geometry within neural networks. We help you uncover the hidden representations inside your model to remove the guesswork from AI training - going from alchemy to precision engineering.
Our Mission
Understand the scientific foundations of neural networks so that we can intentionally design AI.
We believe that AI is the most consequential technology of our time, yet today we train models with remarkably little understanding of the nature of their intelligence.
We’re the research lab dedicated to creating the science and technology to change that.
The platform for intentional model design
Silico lets you build AI models with the precision of written software. See what models have learned, find undesired behavior, and make targeted interventions to improve performance.
Silico works across all types of AI models
The Intentional Design Agenda
Novel methods to understand, debug, and design your AI model
Understand
Reverse engineer the causal mechanisms of AI to reveal its internal structure, uncovering novel science and validating when predictions reflect true understanding.
Discovering a novel class of Alzheimer's biomarkers
Explaining 4.2 million genetic variants
Debug
Precisely debug issues with model behavior, identify and remove confounders, and diagnose failures before they occur in production.
Detecting performative chain-of-thought
Validating whether a cardiac vision model learned real medicine.
Identifying bottlenecks to a robotics model's performance.
Design
Control training precisely to ensure your model learns what you want with less data and fewer off-target effects.
Reducing hallucinations with features as rewards
Accelerating materials discovery with self-correcting search
Intentionally designing the future of AI
Research
We’re investing in fundamental research to uncover how neural networks work at their core.
The World Inside Neural Networks
May 7, 2026
Steering Along Manifolds to Control Neural Networks
May 7, 2026
Interpreting Language Model Parameters
May 5, 2026