Backed by
Combinator
Reticular
Reticular
Pioneering mechanistic understanding to guide biological AI systems with limited data
Our Mission
Data scarcity makes steering biological AI systems difficult.
Reticular builds mechanistic understanding to steer these powerful models with precision - even with limited validation data. We help you:
peer inside biological AI to understand its decision-making
steer models reliably even with limited validation data
accelerate development by reducing expensive real-world testing
build safeguards directly into model capabilities
Our Mission
Data scarcity makes steering biological AI systems difficult.
Reticular builds mechanistic understanding to steer these powerful models with precision - even with limited validation data. We help you:
peer inside biological AI to understand its decision-making
steer models reliably even with limited validation data
accelerate development by reducing expensive real-world testing
build safeguards directly into model capabilities
Our Mission
Data scarcity makes steering biological AI systems difficult.
Reticular builds mechanistic understanding to steer these powerful models with precision - even with limited validation data. We help you:
peer inside biological AI to understand its decision-making
steer models reliably even with limited validation data
accelerate development by reducing expensive real-world testing
build safeguards directly into model capabilities
Our Research
Research forms the foundation of our biological AI interpretability efforts. We focus on:
Translating interpretability techniques from natural language to biological sequence models
Pioneering semantic biological interpretability techniques using DNA & protein annotation databases
Developing and empirically validating sample-efficient guidance algorithms for practical protein engineering use cases
Our Research
Research forms the foundation of our biological AI interpretability efforts. We focus on:
Translating interpretability techniques from natural language to biological sequence models
Pioneering semantic biological interpretability techniques using DNA & protein annotation databases
Developing and empirically validating sample-efficient guidance algorithms for practical protein engineering use cases
Our Research
Research forms the foundation of our biological AI interpretability efforts. We focus on:
Translating interpretability techniques from natural language to biological sequence models
Pioneering semantic biological interpretability techniques using DNA & protein annotation databases
Developing and empirically validating sample-efficient guidance algorithms for practical protein engineering use cases
Partner With Us
We're currently seeking early design partners:
Startups working with biological language models, especially in protein engineering
Research teams exploring mechanistic interpretability
Pharma teams building generative pipelines for antibodies and protein therapeutics
Working with biological generative models? We'd love to chat!
Partner With Us
We're currently seeking early design partners:
Startups working with biological language models, especially in protein engineering
Research teams exploring mechanistic interpretability
Pharma teams building generative pipelines for antibodies and protein therapeutics
Working with biological generative models? We'd love to chat!
Partner With Us
We're currently seeking early design partners:
Startups working with biological language models, especially in protein engineering
Research teams exploring mechanistic interpretability
Pharma teams building generative pipelines for antibodies and protein therapeutics
Working with biological generative models? We'd love to chat!