AI is reshaping industries, redefining customer experiences, and transforming how businesses innovate, operate, and compete. While much of the focus is on frontier models, a fundamental challenge lies in data—how it is stored, retrieved, and made useful for AI applications. The democratization of AI-powered software depends on building on top of the right abstractions, yet today, creating useful, real-time AI applications at scale is not feasible for most organizations.
The challenge isn’t just complexity—it’s trust. AI models are probabilistic, meaning their outputs aren’t deterministic and predictable. This is easily evident in the hallucination problem in chatbots today, and becomes even more critical with the rise of agents, where AI systems make autonomous decisions. Development teams need the ability to control, shape, and ground generated outputs to align with their objectives and ensure accuracy.
AI-powered search and retrieval is a powerful tool that extracts relevant contextual data from specific sources, augmenting AI models to generate reliable and accurate responses or take responsible and safe actions, as seen in the prominent retrieval augmented generation (RAG) approach. At the core of AI-powered retrieval are embedding generation and reranking—two key AI components that capture the semantic meaning of data and assess the relevance of queries and results.
We believe embedding generation and reranking, as well as AI-powered search, belong in the database layer, simplifying the stack and creating a more reliable foundation for AI applications. By bringing more intelligence into the database, we help businesses mitigate hallucinations, improve trustworthiness, and unlock AI’s full potential at scale.
The most impactful applications require a flexible, intelligent, and scalable data foundation. That’s why we’re excited to announce the acquisition of Voyage AI, a leader in embedding and reranking models that dramatically improve accuracy through AI-powered search and retrieval. This move isn’t just about adding AI capabilities—it’s about redefining the database for the AI era.
Why this matters: The future of AI is built on better relevance and accuracy in data
AI is probabilistic—it’s not built like traditional software with pre-defined rules and logic. Instead, it generates responses or takes action based on how the AI model is trained and what data is retrieved. However, due to the probabilistic nature of the technology, AI can hallucinate. Hallucinations are a direct consequence of poor or imprecise retrieval—when AI lacks access to the right data, it generates plausible but incorrect information. This is a critical barrier to AI adoption, especially in enterprises and for mission-critical use cases where accuracy is non-negotiable.
This makes retrieving the most relevant data essential for AI applications to deliver high-quality, contextually accurate results. Today, developers rely on a patchwork of separate components to build AI-powered applications. Sub-optimal choices of these components, such as embedding models, can yield low-relevancy data retrieval and low-quality generated outputs. This fragmented approach is complex, costly, inefficient, and cumbersome for developers.
With Voyage AI, MongoDB solves this challenge by making AI-powered search and retrieval native to the database. Instead of implementing workarounds or managing separate systems, developers can generate high-quality embeddings from real-time operational data, store vectors, perform semantic search, and refine results—all within MongoDB. This eliminates complexity and delivers higher accuracy, lower latency, and a streamlined developer experience.
What Voyage AI brings to MongoDB
Voyage AI has built a world-class AI research team with roots at Stanford, MIT, UC Berkeley, and Princeton and has rapidly become a leader in high-precision AI retrieval. Their technology is already trusted by some of the most advanced AI startups, including Anthropic, LangChain, Harvey, and Replit.
Notably, Voyage AI’s embedding models are the highest-rated zero-shot models in the Hugging Face community. Voyage AI’s models are designed to increase the quality of generated output by:
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Enhancing vector search by creating embeddings that better capture meaning across text, images, PDFs, and structured data.
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Improving retrieval accuracy through advanced reranking models that refine search results for AI-powered applications.
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Enabling domain-specific AI with fine-tuned models optimized for different industries such as financial services, healthcare, and law, and use cases such as code generation.
By integrating Voyage AI’s retrieval capabilities into MongoDB, we’re helping organizations more easily build AI applications with greater accuracy and reliability—without unnecessary complexity.
How Voyage AI will be integrated into MongoDB
We are integrating Voyage AI with MongoDB in three phases. In the first phase, Voyage AI’s text embedding, multi-modal embedding, and reranking models will remain widely available through Voyage AI’s current APIs and via the AWS and Azure Marketplaces—ensuring developers can continue to use their best-in-class embedding and reranking capabilities. We will also invest in the scalability and enterprise readiness of the platform to support the increased adoption of Voyage AI’s models.
Next, we will seamlessly embed Voyage AI’s capabilities into MongoDB Atlas, starting with an auto-embedding service for Vector Search, which will handle embedding generation automatically. Native reranking will follow, allowing developers to boost retrieval accuracy instantly. We also plan to expand domain-specific AI capabilities to better support different industries (e.g., financial services, legal, etc.) or use cases (e.g., code generation).
Finally, we will advance AI-powered retrieval with enhanced multi-modal capabilities, enabling seamless retrieval and ranking of text, images, and video. We also plan to introduce instruction-tuned models, allowing developers to refine search behavior using simple prompts instead of complex fine-tuning. This will be complemented by embedding lifecycle management in MongoDB Atlas, ensuring continuous updates and real-time optimization for AI applications.
What this means for developers and businesses
AI-powered applications need more than a database that just stores, processes, and persists data—they need a database that actively improves retrieval accuracy, scales seamlessly, and eliminates operational friction. With Voyage AI, MongoDB redefines what’s required for a database to underpin mission-critical AI-powered applications.
Developers will no longer need to manage external embedding APIs, standalone vector stores, or complex search pipelines. AI retrieval will be built into the database itself, making semantic search, vector retrieval, and ranking as seamless as traditional queries.
For businesses, this translates to faster time-to-value and greater confidence in scaling AI applications. By delivering high-quality results at scale, enterprises can seamlessly integrate AI into their most critical use cases, ensuring reliability, performance, and real-world impact.
Looking ahead: What comes next
This is just the beginning. Our vision is to make MongoDB the most powerful and intuitive database for modern, AI-driven applications.
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Voyage AI’s models will soon be natively available in MongoDB Atlas.
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We will continue evolving MongoDB’s AI retrieval capabilities, making it smarter, more adaptable, and capable of handling a wider range of data types and use cases.
Stay tuned for more details on how you can start using Voyage AI’s capabilities in MongoDB.
To learn more about how MongoDB and Voyage AI are powering state-of-the-art AI search and retrieval for building, scaling, and deploying intelligent applications, visit our product page.
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