As businesses amass ever-growing troves of unstructured customer data - including documents, PDFs, images, videos, and audio files - the challenge of extracting meaningful insights from this "dark data" has become increasingly critical. Traditional database approaches simply cannot handle the complexity and diversity of multi-modal enterprise content.

Vector search technologies have emerged as one of the first solutions, allowing organizations to embed and index these varied data sources en masse. This enables users to retrieve relevant files based on natural language queries, akin to the Retrieval Augmented Generation (RAG) workflow. However, this represents only the first step in realizing the full potential of multi-modal data.

Embeddings are not Enough

While vector search provides a valuable coarse-grained retrieval capability, it has inherent limitations. Condensing an entire document or multiple paragraphs into a single vector representation often fails to capture the nuanced content and context that enterprise users require. Extracting precise information - such as a specific sales figure, the author of a report, or the insights contained in a data visualization - remains a significant challenge. Overcoming this requires more sophisticated indexing and analysis approaches that can parse the diverse modalities within enterprise data.

Transforming Visual Content with VLM-Run

We believe Visual Language Models (VLMs) hold the key to unlocking the true value of enterprise visual content. Enter VLM-1 - our highly specialized Vision Language Model that empowers organizations to accurately extract structured data from diverse visual sources such as images, documents, and presentations. This breakthrough capability, which we call ETL for visual content, allows businesses to seamlessly process and index unstructured visual data, transforming raw multi-modal information into valuable, queryable insights.

Pairing VLM-1 with a Flexible Data Platform

To fully capitalize on the power of VLM-1, enterprises require a data platform that can handle the scale, diversity, and flexible schema of the extracted visual insights. This is where a modern, document-oriented NoSQL database like MongoDB excels.

MongoDB's support for JSON-like documents and flexible schema make it an ideal complement to VLM-1. By storing the structured data extracted from visual content directly in MongoDB, organizations can seamlessly query and analyze this information alongside their other multimodal business data. The managed MongoDB Atlas platform further enhances this integration, providing enterprise-grade reliability, scalability, and ease of use.

MongoDB: The Perfect Fit for VLM-1

MongoDB is a document-oriented NoSQL database that supports JSON-like documents utilizing a flexible schema. It is designed for scalability, flexibility, and performance, making it a popular choice for modern applications incorporating a lot of unstructured and multi-modal data. Since VLM-1 can extract structured JSON from visual content, MongoDB and the managed MongoDB Atlas platform are a natural fit for storing and querying this structured data.This is why we're excited to announce our official partnership with MongoDB as part of their AI partners ecosystem.

Get Started with VLM-1 and MongoDB

If you're eager to experience the transformative potential of VLM-1 and MongoDB, we've created a step-by-step Colab notebook that walks through the integration process. Dive in and see how you can elevate your enterprise's visual content into a strategic advantage.