Goodfire
Uncovers hidden representations inside neural networks to understand, debug, and design AI models and enable targeted interventions
The product is an interpretability platform that helps uncover hidden representations and internal structure inside neural networks to understand, debug, and design AI models. It addresses guesswork in training by revealing what models have learned, identifying undesired behavior and confounders, and enabling targeted interventions. It is for AI research and engineering teams and developers building foundation models in life sciences, robotics, vision, and language. It is delivered as a SaaS platform with domain-specific versions for life sciences, robotics & vision, and LLMs.
Key features
- Uncover hidden representations inside models
- Reverse engineer causal mechanisms
- Reveal internal structure and geometry
- See what models have learned
- Find undesired behavior
- Make targeted interventions to improve performance
- Identify and remove confounders
- Diagnose failures before production
- Control training precisely
- Validate predictions reflect true understanding
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- Engineering teams
Recent go-to-market activity
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