Building AI tools and software to replace the Design-Build-Test-Learn (DBTL) cycle in synthetic biology — an in silico model-based AI for all bioinformatics, biotechnology, and synthetic biology.
Our AI platform replaces the traditional Design-Build-Test-Learn (DBTL) cycle in synthetic biology. Users input questions about their biological data and receive AI-powered answers — from experiment design to protein simulation — all driven by published literature and proprietary databases.
Beyond experiment design, the platform handles plasmid construction, genetic modification guides, experimental protocol generation, and protein generation & simulation. It is an in silico model-based AI for all bioinformatics, biotechnology, and synthetic biology that works from papers and data.
Summarizes biological papers, answers questions from proprietary bio databases, and designs experiments based on all published literature.
AI-powered plasmid design tool for construct optimization, genetic modification guides, and streamlined cloning workflows.
Protein generation, structure prediction, molecular simulation, and enzyme optimization using transformer-based language models.
Automated protocol generation, DBTL cycle acceleration, and data-driven experiment optimization to minimize time from hypothesis to result.
AI-powered automated control of temperature, humidity, and lighting in our biodiversity farm — creating exact replicas of natural environments for each species. All species are housed in vivarium systems with continuous monitoring, and zoo registration is planned soon.
AI automatically controls temperature, humidity, and lighting cycles to replicate the exact natural habitats each species requires for optimal health and behavior.
All species housed in purpose-built vivarium systems with automated monitoring, health tracking, and environmental logging. Zoo registration currently in progress.