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A multimodal AI infrastructure company offering state-of-the-art embedding models and Vision-Language models to power high-performance search and retrieval applications.
Provides high-performance text and multimodal embedding models (v5 Omni) for dense vector retrieval Offers Vision-Language models (Jina-VLM) capable of deep image understanding and reasoning Delivers a scalable API-based infrastructure for RAG (Retrieval-Augmented Generation) pipelines Supports long-context processing to handle massive document retrieval tasks Facilitates cross-modal search, allowing users to query images with text or vice versa
Offers industry-leading benchmarks for retrieval accuracy and multimodal performance Provides a seamless, API-first experience that integrates into modern LLM stacks Handles complex multimodal data (image+text) better than standard single-modality alternatives Optimizes for low latency and high throughput, crucial for production enterprise applications Maintains a commitment to open-weights models, fostering community trust and adoption
Category: AI & Automation
Team Size: 51-200
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Jina AI is an AI infrastructure provider that bridges the gap between raw data and actionable intelligence through its high-performance multimodal models. The company is best known for its Jina Embeddings v5 Omni and Jina-VLM models, which enable businesses to build advanced search, retrieval-augmented generation (RAG), and multimodal analysis systems. Jina AI focuses on high efficiency, large context handling, and versatility, allowing enterprise developers to process complex data types—including text, images, and audio—at scale.
Jina AI was founded in 2020 in Berlin, Germany, by Han Xiao, formerly the Head of AI/ML at Zalando, to democratize multimodal AI search. Recognizing that most businesses struggled to handle unstructured data effectively, the team focused on building efficient infrastructure that allows developers to search across any modality with ease.
