Unsiloed AI
AI teams spend 6+ months building document workflows, yet fewer than 10% ever reach production. Generic LLM parsers and OCR collapse on multimodal documents with text, tables, images, and charts. Poor parsing and suboptimal chunking cripple RAG pipelines and downstream automation. Unsiloed AI has built state-of-the-art vision models which serves as the infrastructure layer for turning unstructured data into structured, queryable, and LLM-ready assets. Our APIs are already parsing hundreds of tho
Y Combinator
yc-company-directory #179AI teams spend 6+ months building document workflows, yet fewer than 10% ever reach production. Generic LLM parsers and OCR collapse on multimodal documents with text, tables, images, and charts. Poor parsing and suboptimal chunking cripple RAG pipelines and downstream automation. Unsiloed AI has built state-of-the-art vision models which serves as the infrastructure layer for turning unstructured data into structured, queryable, and LLM-ready assets. Our APIs are already parsing hundreds of tho
sourceUnsiloed AI 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 Unsiloed AI
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.
Tavus
match 96At Tavus, we're building the human layer of AI. Our mission is to make human-AI interaction as natural as face-to-face interaction, enabling the human touch where it has been previously unscalable. We achieve this through pioneering research in multi-modal AI models for human perception and understanding, combined with state-of-the-art human avatar rendering and conversation models that we make accessible to developers worldwide. Our models and APIs power everything from text-to-video AI avatars
shared extracted concepts: apis; shared product terms: layer, model, state, art; shared product theme: developer infrastructure; shared affiliation: Y Combinator
Trieve
match 82Infrastructure for search teams building retrieval and RAG. Trieve combines search language models with tools for tuning ranking and relevance. Building excellent search is difficult and can take months to implement then even more time to maintain. Trieve offers production-ready infrastructure that works out of the box to help search teams build adjustable AI search and RAG into their products. With tools for custom models, relevancy weighting, date-recency biasing, semantic full-text hybrid sea
shared extracted concept terms: model, rag; shared product terms: infrastructure, rag, model, month; shared product theme: developer infrastructure; shared affiliation: Y Combinator
Miru
match 82Miru makes it easy for robotics teams to manage, version, and deploy robot configurations. Developers use our well-documented APIs and UI components to automate configuration updates for both engineers and operators. In the long term, we're building software infrastructure to accelerate the automation of the physical world.
shared extracted concept terms: vision, pipeline; shared product terms: document, apis, infrastructure, automation; shared product theme: workflow automation, developer infrastructure; same category: AI agents; shared affiliation: Y Combinator
OnDeck AI
match 74OnDeck 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: vision, model; shared product terms: infrastructure, layer, vision, model; shared product theme: developer infrastructure; shared affiliation: Y Combinator
ideate.xyz
match 70Generate. Paint. Refine. ideate.xyz is an AI-powered 3D art suite on the web, taking your ideas from concept to production ready assets in record time. Our powerful text to image to 3D pipeline rapidly generates model assets using the latest AI tech. Rapidly generate and edit 3D model textures in your browser using our texture projection and inpainting editor. Get your 3D assets production ready using our texture processing, upscaling and compression algorithms.
shared product terms: art, production, ready, asset; shared product theme: workflow automation; shared affiliation: Y Combinator
Captain
match 69Captain delivers the most accurate file search engine built for AI agents. We’ll index data from the sources folks already use like S3, SharePoint, and Google Drive, and easily scale multimodal, petabyte-level content search. We’re the Snowflake for Unstructured Data. Captain tops the Open-RAG-Benchmark with over 20% higher accuracy than standard RAG pipelines. We achieve this through robust data processing techniques like embedding normalization across modalities, ensuring that representations
shared extracted concept terms: rag, pipeline; shared product terms: already, multimodal, unstructur, rag; shared product theme: workflow automation; same category: AI agents; shared affiliation: Y Combinator