Goodfire
Goodfire helps AI researchers understand and control LLM internals through interpretability.
Goodfire is an AI interpretability platform that enables researchers and engineers to understand and control the internal mechanisms of large language models like Llama 3.3 70B. The company offers Ember, a hosted API for mechanistic interpretability research that lets users shape model behavior by directly controlling internal features, and Silico, a tool for adjusting model parameters during training. By reverse-engineering foundation models through Sparse Auto Encoders, Goodfire has demonstrated real-world impact—reducing hallucinations by 58% at ~90x lower cost than traditional methods—making it the first commercial product in the mechanistic interpretability space.
Problem solved
AI models remain black boxes, making it impossible to debug hallucinations, understand model behavior, or ensure safety and fairness without expensive retraining or external evaluation frameworks.
Target customer
AI research teams, healthcare/biotech companies deploying foundation models, enterprise organizations needing trustworthy AI systems, academic research institutions studying model behavior.
Founders
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Eric Ho
CEO & Co-Founder
Previously founded and led RippleMatch (2016-2024) to $10M+ ARR as CEO, scaling AI-driven hiring platform with backing from Goldman Sachs and G2 Venture Partners; Yale graduate.
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Dan Balsam
CTO & Co-Founder
Founding engineer and Head of AI at RippleMatch where he built the core engineering organization and deployed LLMs in production.
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Tom McGrath
Chief Scientist & Co-Founder
Senior Research Scientist at Google DeepMind where he founded the interpretability team; PhD completed in 2016 with early conviction in AI's importance.
Funding history
Seed
$7M
August 2024
Led by Lightspeed Venture Partners
· Menlo Ventures, South Park Commons, Work-Bench, Juniper Ventures, Mythos Ventures, Bluebirds Capital, angels
Series A
$50M
April 2025
Led by Menlo Ventures
· Lightspeed Venture Partners, Anthropic, B Capital, Work-Bench, Wing, South Park Commons
Series B
$150M
February 2026
Led by B Capital
· Juniper Ventures, Menlo Ventures, Lightspeed Venture Partners, South Park Commons, Wing Venture Capital, DFJ Growth, Salesforce Ventures, Eric Schmidt
Total raised:
$207M
Pricing
Custom pricing determined on case-by-case basis according to customer requirements. No publicly disclosed pricing tiers.
Notable customers
Prima Mente Inc. (healthcare AI for Alzheimer's detection), Arc Institute, Mayo Clinic, Microsoft
Integrations
Llama 3.3 70B, Arc Institute, Mayo Clinic, Microsoft, Google DeepMind (research partnership)
Tech stack
Google Font API (Font scripts)
Google Workspace (Email)
Amazon S3 (CDN)
Amazon Cloudfront (CDN)
Amazon Web Services (PaaS)
AWS Certificate Manager (SSL/TLS certificate authorities)
Website
Competitors
DeepSeek / OpenAI Interpretability Research
Academic/internal research efforts lack commercial product offering and API access for practitioners.
Anthropic Constitutional AI
Focuses on constitutional methods and RLHF rather than mechanistic reverse-engineering of model internals.
Redwood Research
Focuses on mechanistic interpretability research but hasn't commercialized into accessible products like Ember.
Why this matters: Goodfire commercializes mechanistic interpretability—a breakthrough AI safety/transparency research area—into the first practical product suite (Ember and Silico) for controlling model behavior. With $207M raised and backing from Anthropic, Menlo, and B Capital, plus real-world results in healthcare (Alzheimer's biomarker discovery), the company is establishing the category for AI interpretability as a critical infrastructure layer in enterprise AI deployment.
Best for: AI research teams, biotech/healthcare companies, and enterprises that need to understand model behavior, reduce hallucinations, and ensure trustworthy AI deployments without expensive retraining cycles.
Use cases
Reducing Hallucinations in Medical AI
Healthcare organizations use Goodfire to peer into epigenetic foundation models and identify novel biomarkers while reducing false positives. Prima Mente used Goodfire's interpretability tools to discover previously unknown Alzheimer's detection biomarkers and validate model decisions for clinical use.
Model Debugging and Safety Validation
Engineering teams directly control internal model features via Ember to debug unwanted behaviors and verify safety properties without retraining. This enables 58% reduction in hallucinations at 90x lower cost than traditional LLM-as-judge evaluation methods.
Foundation Model Customization During Training
Research organizations use Silico to adjust model parameters and behavior during training based on mechanistic insights. This allows fine-grained control over model outputs and properties before deployment, enabling more efficient and targeted model adaptation.
Alternatives
OpenAI Interpretability Research
Academic research without commercial API products; limited external access to mechanistic tools.
Anthropic Interpretability
Integrated into Anthropic's own models via constitutional AI methods rather than offered as standalone tool for other models.
Traditional RLHF / Fine-tuning
Slower, more expensive, less interpretable approach to model behavior modification without mechanistic understanding.
FAQ
What does Goodfire do? +
Goodfire is an AI interpretability platform that helps researchers and engineers understand and control the internal mechanisms of large language models. It offers Ember (a hosted API for mechanistic interpretability research) and Silico (a tool for adjusting parameters during training), both built on Sparse Auto Encoders that reverse-engineer model internals to enable precise behavior control and debugging.
How much does Goodfire cost? +
Goodfire uses custom, case-by-case pricing determined by customer requirements and use case scope. Contact their team directly for pricing details.
What are alternatives to Goodfire? +
Alternatives include OpenAI's mechanistic interpretability research (academic, limited access), Anthropic's constitutional AI methods (integrated into their models), and traditional RLHF/fine-tuning approaches (slower, less interpretable). None currently offer a dedicated commercial product for reverse-engineering model internals like Goodfire does.
Who uses Goodfire? +
Target customers include AI research teams, biotech/healthcare companies deploying foundation models (like Prima Mente), enterprise organizations requiring trustworthy AI, and academic institutions. Notable partners include Arc Institute, Mayo Clinic, and Microsoft.
How does Goodfire compare to traditional model evaluation methods? +
Goodfire's mechanistic approach reduces hallucinations 58% more efficiently than LLM-as-judge evaluation at ~90x lower cost, with no standard benchmark degradation. Rather than external evaluation, it peers inside model internals to identify and control problematic features, enabling permanent behavior improvements instead of temporary fixes.
Tags
AI interpretability
mechanistic interpretability
Sparse Auto Encoders
model debugging
model safety
foundation models
trustworthy AI