Jina AI provides a search foundation for developers building multimodal AI applications. Their platform offers APIs for advanced web content extraction, conversion to structured formats like JSON and Markdown, and integration with LLMs. This enables robust data ingestion and processing pipelines for complex search and reasoning tasks.
Funding
$37.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
CPGCSIYPFounders
Product
Problem
Many businesses and developers struggle to efficiently retrieve relevant information from large, complex datasets, hindering search accuracy and overall user experience. Existing search solutions often fail to effectively handle multimodal data, such as text and images, limiting their applicability in diverse search scenarios.
Solution
Jina AI provides a neural search platform designed to enable businesses and developers to create and deploy multimodal search solutions. The platform leverages advanced embeddings and retrieval models to address the challenges of information retrieval from large datasets. Jina AI's models form the search foundation for high-quality enterprise search and Retrieval-Augmented Generation (RAG) systems. The platform offers world-class multimodal multilingual embeddings and a neural retriever to maximize search relevancy.
Target Audience
The primary users are businesses and developers seeking to enhance search accuracy and user experience by efficiently retrieving relevant information from large datasets, particularly those involving multimodal data.
Features
- Multilingual Multimodal Embeddings: Supports text and images in multiple languages.
- Neural Retriever: Maximizes search relevancy.
- Reader: Converts URLs to LLM-friendly input by simply adding `r.jina.ai` in front.
- Classifier: Zero-shot and few-shot classification for image and text.
- Segmenter: Cuts long text into chunks and performs tokenization.
- L2 Normalization: Scales embeddings so their Euclidean (L2) norm becomes 1, preserving direction.
- Data Type Options: Returns data as float, binary, or base64 encoding.