What Goodfire does
Goodfire is a San Francisco-based AI research company focused on mechanistic interpretability—using insights from how neural networks represent and compute internally to understand, debug, and steer model behavior. Its public positioning emphasizes moving beyond “black box” inputs/outputs so that teams can both diagnose failures before deployment and intentionally design what models learn and how they behave.
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On the product side, Goodfire offers Silico, a “interpretability agent” desktop product designed to turn research questions into inspectable experiments and reports, including support for running long-horizon interpretability workflows at “frontier scale.” Goodfire also describes its interpretability platform as Ember, which is presented as the system that decodes model internal mechanisms to provide programmable access to internal computations for users and partners. Across its messaging and blog, Goodfire targets organizations working on frontier foundation models and applied domains (e.g., life sciences, robotics/vision) that need interpretability to improve safety, reliability, and scientific understanding rather than relying only on external evaluation.