Goodfire

What Goodfire does

Goodfire is a research company and public benefit corporation dedicated to using interpretability to understand, learn from, and design AI systems. They help customers across industries turn AI into something that can be understood, debugged, and shaped like software. Goodfire aims to advance the science of how AI systems actually work, moving beyond treating models as black boxes to steer what models learn, make them safer and more useful, and extract vast knowledge. Their mission is to build the next generation of safe and powerful AI by understanding the intelligence being built.

Products

• SilicoA platform for intentional model design that allows users to build AI models with the precision of written software. It enables seeing what models have learned, finding undesired behavior, and making targeted interventions to improve performance. Silico incorporates frontier interpretability techniques, a 'model neuroscientist' agent for experiments, and a model design environment for teams.

Key capabilities

• Frontier interpretability techniques
• Model neuroscientist (agent that plans and runs experiments, returns results, and learns over time)
• Model design environment for training and debugging models
• Predict: See inside every prediction, decompose models into interpretable features
• Check Health: Run comprehensive diagnostics on internal representations
• Debug: Debug model failures
• Improve: Shape model behavior to enhance capabilities
• Generalize: Target specific learned structures to generalize with less data

Use cases

• Extracting novel insights from models trained on scientific data
• Reverse engineering causal mechanisms of AI to reveal internal structure
• Discovering novel Alzheimer's biomarkers by interpreting epigenetic models
• Decoding internal representations of genomic models (e.g., Evo 2)
• Predicting how genetic variants cause disease using Evo 2 embeddings
• Detecting performative chain-of-thought in LLMs
• Validating cardiac vision models for real clinical understanding
• Identifying bottlenecks in robotics model performance
• Reducing hallucinations in LLMs by guiding model training with interpretability
• Accelerating materials discovery with self-correcting search in diffusion models
• Understanding and debugging AI models
• Intentionally designing AI models
• Training or fine-tuning foundation models across architectures and modalities

AI approach

Goodfire is a research company focused on interpretability to understand, debug, and design AI systems. Their platform, Silico, helps users uncover hidden representations within neural networks to improve model performance and make targeted interventions. They aim to move AI training from 'alchemy to precision engineering' by providing tools to analyze, diagnose, and shape model behavior.

Tech named: neural networks, interpretability, foundation models, epigenetic model, genomic model, diffusion model, LLM, vision model, latent space, sparse autoencoders

Industries served

• Life sciences
• Healthcare
• Robotics
• Materials science

What it says sets it apart

• Focus on interpretability to understand, debug, and design AI systems
• Team shaped modern neural network interpretability at OpenAI, DeepMind, Stanford, and Harvard
• Backed by over $200M from leading investors
• Advancing the science of how AI systems actually work
• Treating models as black boxes is an unnecessary handicap
• Goal to make AI that can be understood, debugged, and shaped like software
• Pioneered core research directions in interpretability (e.g., sparse autoencoders, automated feature interpretation)

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.