Osmosis
Osmosis is a post-training platform that helps companies fine-tune language models using reinforcement learning. We work with fast-growing AI companies to train task/domain-specific models that beat foundation models on performance, cost, and latency. Our platform handles compute orchestration, reward modeling, and training run observability as a CLI-based product usable by developers and agents.
Y Combinator
yc-company-directory #5771Osmosis is a post-training platform that helps companies fine-tune language models using reinforcement learning. We work with fast-growing AI companies to train task/domain-specific models that beat foundation models on performance, cost, and latency. Our platform handles compute orchestration, reward modeling, and training run observability as a CLI-based product usable by developers and agents.
sourceOsmosis is classified as AI agents 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 Osmosis
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.
TensorPool
match 84Our CLI makes ML model training effortless - just describe your job, and we handle GPU orchestration and execution at half the cost of major cloud providers.
shared extracted concept terms: orchestration, model, train; shared product terms: train, model, cost, handle; shared product theme: developer infrastructure; shared affiliation: Y Combinator
Baud
match 84Novel AI hardware and developer platform, built from first principles to let companies train, fine-tune, and deploy frontier models with orders of magnitude better performance than incumbents
shared extracted concepts: companies; shared product terms: developer, companie, train, fine; shared product theme: developer infrastructure; shared affiliation: Y Combinator
Strand AI
match 71Strand AI develops foundation models to generate missing bio-data about patients. With this imputed data, pharmaceutical companies can select better patients for their drug trials and shave months from their drug launch timelines. We’ve already trained a multimodal foundation model that integrates spatial biology modalities, beating SOTA at a fraction of the cost.
shared extracted concepts: foundation models; shared product terms: train, companie, model, beat; shared affiliation: Y Combinator
RunRL
match 66RunRL improves LLMs and AI agents with reinforcement learning. Do you have a model that needs to get better at a certain task? Are you tired of messing around with prompts? Do you spend a lot of money on observability, and wish that all this data could let your model self-improve? Well, head on over to RunRL.com, and see what we have to offer! If you give us a model, a prompt, and a reward, we’ll make your model’s reward go up.
shared extracted concepts: reinforcement learning; shared product terms: model, reinforcement, task, reward; same category: AI agents; shared affiliation: Y Combinator
Roboflow
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shared extracted concept terms: foundation, model; shared product terms: post, fine, model, foundation; shared product theme: developer infrastructure; shared affiliation: Y Combinator
OnDeck AI
match 66OnDeck is the infrastructure layer that makes Vision Language Models accessible and scalable for enterprise. We let organizations instantly find any object, behavior or event, in any footage, without needing to train a model or collect any training data. The Pain: Creating vision models usually takes months: collecting training data, training, then deployment. Worse yet: + it’s often impossible to get enough data for a specific task, and + even the best cv models struggle to generalize across di
shared extracted concept terms: language, model; shared product terms: language, model, train, specific; shared product theme: developer infrastructure; shared affiliation: Y Combinator