UrbanKisaan
UrbanKisaan is an AI-for-plant-biology company. We build foundation models and the physical infrastructure that trains them. Shipping a single new seed variety takes about ten+ years. Two on germplasm, three on crosses and selection, three or four on field trials, one on regulatory. Inside that decade, prediction and experiment never properly meet. DNA is abundant, phenotype data is scarce and unpaired, and the cycle never trains itself. We closed that loop. Three foundation models cover the ups
Y Combinator
yc-company-directory #4252UrbanKisaan is an AI-for-plant-biology company. We build foundation models and the physical infrastructure that trains them. Shipping a single new seed variety takes about ten+ years. Two on germplasm, three on crosses and selection, three or four on field trials, one on regulatory. Inside that decade, prediction and experiment never properly meet. DNA is abundant, phenotype data is scarce and unpaired, and the cycle never trains itself. We closed that loop. Three foundation models cover the ups
sourceUrbanKisaan is classified as AI infrastructure from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to UrbanKisaan
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
Blank Bio
match 64Blank Bio is an applied AI research lab focused on increasing the success rates of clinical trials. We do this by training RNA foundation models that learn the patterns that shape disease progression and patient response to treatment. We aim to help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique. We’re a technical team of AI scientists and engineers from companies including Recursion, Deep Genomics, DeepMind, and Amazon, an
shared extracted concept terms: foundation, model; shared product terms: trial, train, foundation, model; same category: AI infrastructure; shared affiliation: Y Combinator
Reticular
match 54Reticular is scaling polygenic prediction for embryo selection, helping IVF couples plan their families today while building a platform to unlock cures for complex heritable diseases long-term. John competed Biology Olympiads before spending 4 years at MIT publishing ML/bio research in NeurIPS and Nature. We believe biological models encode far more information than anyone is currently using - our goal is to unlock this potential.
shared extracted concept terms: model; shared product terms: prediction, selection, biology, year; same category: AI infrastructure; shared affiliation: Y Combinator
Anthrogen
match 52Proteins power everything from the cells in your body to creating materials you rely on every day—but until now, we’ve been forced to discover their functions by trial and error. Designing a new therapeutic can take decades and billions of dollars, and even our best industrial catalysts work at a snail’s pace compared to their theoretical optimums. Anthrogen is changing that. By training massive AI foundation models on protein sequences and structures, we’ve unlocked the ability to generate—on d
shared product terms: trial, take, decade, train; same category: AI infrastructure; shared affiliation: Y Combinator
Synthetic Sciences
match 49We're building the infrastructure for the era of autonomous science. Our core thesis: scientific foundation models with real research "taste" require two things, built in parallel. A product that captures high-quality organic research-process data at scale, and the training and research to turn that data into models with genuine scientific "taste". We're building both.
shared product terms: infrastructure, foundation, model, two; shared product theme: developer infrastructure; same category: AI infrastructure; shared affiliation: Y Combinator