Smarter AI Search, Powered by MongoDB Atlas and Pureinsights

Prasad Pashte and Kamran Khan (CEO, Pureinsights)
October 1, 2025

We’re excited to announce that the integration of MongoDB Atlas with the Pureinsights Discovery Platform is now generally available—bringing to life a reimagined search experience powered by keyword, vector, and gen AI.

What if your search box didn’t just find results, but instead understood intent? That’s exactly what this integration delivers!

Beyond search: From matching to meaning

Developers rely on MongoDB’s expansive knowledge ecosystem to find answers fast. But even with a rich library of technical blogs, forum threads, and documentation, traditional keyword search often falls short—especially when queries are nuanced, multilingual, or context-driven.

That’s where the MongoDB-Pureinsights solution shines. Built on MongoDB Atlas and orchestrated by the Pureinsights Discovery platform, this intelligent search experience starts with the fundamentals: fast, accurate keyword results, powered by MongoDB Atlas Search.

But as queries grow more ambiguous—say, “tutorials for AI”—the platform steps up. MongoDB Atlas Vector Search with Voyage AI, available as an embedding and reranking option (now part of MongoDB), goes beyond literal keywords to interpret intent—helping applications deliver smarter, more relevant results. The outcome: smarter, semantically aware responses that feel intuitive and accurate—because they are.

What’s more, with generative answers enabled, the platform synthesizes information across MongoDB’s ecosystem (blog content, forums, and technical docs) to deliver clear, contextual answers using state-of-the-art language models. But it's not just pointing you to the right page. Instead, the platform is providing the right answer, with citations, ready to use. It’s like embedding a domain-trained AI assistant directly into your search bar.

“As organizations look to move beyond traditional keyword search, they need solutions that combine speed, relevance, and contextual understanding,” said Haim Ribbi, Vice President, Global CSI, VAR & Tech Partner at MongoDB. “MongoDB Atlas provides the foundation for smarter discovery, and this collaboration with Pureinsights shows how easily teams can deliver gen AI-powered search experiences using their existing content.”

Built for users everywhere

But intelligence alone doesn’t make it transformational. What sets this experience apart is its adaptability. Whether you’re a developer troubleshooting in Berlin or a product owner building in São Paulo, the platform tailors responses to your preferences.

Prefer concise summaries or deep technical dives? Want to translate answers in real time? Need responses that reflect your role and context? You’re in control. From tone and length to language and specificity, this is a search that truly understands you—literally and figuratively.

Built on MongoDB. Elevated by Voyage AI. Delivered by Pureinsights.

At the core of this solution is MongoDB Atlas, which unifies fast, scalable data access to structured content through Atlas Search and Atlas Vector Search. Looking ahead, by integrating with Voyage AI’s industry-leading embedding models, MongoDB Atlas aims to make semantic search and retrieval-augmented generation (RAG) applications even more accurate and reliable. While currently in private preview, this enhancement signals a promising future for developers building intelligent, AI-powered experiences.

Pureinsights handles the orchestration layer. Their Discovery Platform ingests and enriches content, blends keyword, vector, and generative search into a seamless UI, and integrates with large language models like GPT-4. The platform supports multilingual capabilities, easy deployment, and enterprise-grade scalability out of the box. While generative answers are powered by integrated large language models (LLMs) and may vary by deployment, the solution is enterprise-ready, cloud-native, and built to scale.

Bringing intelligent discovery to your own data

Watch the demo video to see AI-powered search in action across 4,000+ pages of MongoDB content—from community forums and blog posts to technical documentation.

While the demo features MongoDB’s content, the solution is built to adapt. You can bring the same AI-powered experience to your internal knowledge base, customer support portal, or developer hub—no need to build from scratch.

Visit our partner page to learn more about MongoDB and Pureinsights and how we’re helping enterprises build smarter, AI-powered search experiences. Apply for a free gen AI demo using your enterprise content.

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Charting a New Course for SaaS Security: Why MongoDB Helped Build the SSCF

The way companies everywhere work is powered by SaaS. From collaboration tools to critical infrastructure, organizations rely on SaaS applications to drive their business forward. But this widespread adoption has created a significant security blind spot. How can you ensure every one of these applications is configured securely when they all offer different settings, capabilities, and levels of visibility? This inconsistency creates friction, wastes resources, and ultimately, exposes businesses to unnecessary risk. At MongoDB, we believe that securing the SaaS ecosystem is a shared responsibility. That's why we were proud to collaborate with the Cloud Security Alliance (CSA) and industry leaders like GuidePoint Security to develop a new standard—the SaaS Security Capability Framework (SSCF) . The problem: A gap in cloud security For years, the majority of security assessments have focused on the SaaS provider's organizational security, often through frameworks like SOC 2 or ISO 27001. While essential, these frameworks don't always address a critical question: what security capabilities are available to the SaaS customer within the application? This gap means that security teams face a chaotic landscape. Every new SaaS app brings a different set of configurable controls for logging, identity management, and data access. This makes it nearly impossible to implement and track consistent security policies at scale, leading to a burdensome assessment process for everyone involved. The solution: A common framework for SaaS security The SSCF was created to solve this problem by establishing a clear, technical set of customer-facing security controls that SaaS vendors should provide. The framework is designed to empower customers by ensuring they have the tools they need to operate applications securely at scale on their side of the Shared Security Responsibility Model (SSRM). The framework helps with many use cases, but three key audiences stand out: For risk management teams: The SSCF provides a clear baseline to use during vendor assessments, simplifying procurement. For SaaS security teams: It offers a checklist for implementing the security features enterprises expect, streamlining the security program. For SaaS vendors: The SSCF standardizes assessment responses, reducing the overhead of custom questionnaires and helping vendors meet customer requirements. The SSCF focuses on six critical domains, aligned with CSA’s Cloud Control Matrix, providing specific and actionable controls for each: Change Control and Configuration Management (CCC): Ensuring you can programmatically query and get documentation on all security configurations. Data Security and Privacy Lifecycle Management (DSP): Giving customers control over features like disabling file uploads to prevent malicious code. Identity and Access Management (IAM): Providing robust, modern controls for user access, including SSO enforcement, non-human identity (NHI) governance, and a dedicated read-only security auditor role. Interoperability and Portability (IPY): Giving administrators control over mass data exports and visibility into application integrations. Logging and Monitoring (LOG): Defining a clear set of comprehensive requirements for machine-readable logs with mandatory fields for effective threat detection and forensics. Security Incident Management (SEF): Requiring a simple, effective way for vendors to notify a designated customer security contact during an incident. MongoDB's commitment to a more secure ecosystem Our involvement in creating the SSCF stems from our deep commitment to the security of our customers' data and the broader developer community. We believe that robust security shouldn't be an afterthought; it must be built in and easy to consume. The principles outlined in the SSCF—like strong identity controls and comprehensive logging—are philosophies we already built into our own data platform. Strong security capabilities allow our customers to build and innovate faster and more securely, knowing they have a reliable foundation. And personally, as a co-chair of the CSA SSCF, I’ve seen great excitement and engagement on the part of our working group—which helped me realize how many companies are affected by this lack of consistency. The SSCF is a vital step toward creating a more trusted, efficient, and secure global SaaS ecosystem. We are thrilled to have been a part of this foundational work and will continue to champion this standard that empowers developers and security teams alike. Visit our security page to learn more about how MongoDB helps protect your data.

September 30, 2025

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Top Considerations When Choosing a Hybrid Search Solution

Search has evolved. Today, natural language queries have largely replaced simple keyword searches when addressing our information needs. Instead of typing “Peru travel guide” into a search engine, we now ask a large langauge model (LLM) “Where should I visit in Peru in December during a 10-day trip? Create a travel guide.” Is keyword search no longer useful? While the rise of LLMs and vector search may suggest that traditional keyword search is becoming less prevalent, the future of search actually relies on effectively combining both methods. This is where hybrid search plays a crucial role, blending the precision of traditional text search with the powerful contextual understanding of vector search. Despite advances in vector technology, keyword search still has a lot to contribute and remains essential to meeting current user expectations. The rise of hybrid search By late 2022 and particularly throughout 2023, as vector search saw a surge in popularity (see image 1 below), it quickly became clear that vector embeddings alone were not enough. Even as embedding models continue to improve at retrieval tasks, full-text search will always remain useful for identifying tokens outside the training corpus of an embedding model. That is why users soon began to combine vector search with lexical search, exploring ways to leverage both precision and context-aware retrieval. This shift was driven in large part by the rise of generative AI use cases like retrieval-augmented generation (RAG), where high-quality retrieval is essential. Figure 1. Number of vector search vendors per year and type. As hybrid search matured beyond basic score combination, the main fusion techniques emerged - reciprocal rank fusion (RRF) and relative score fusion (RSF). They offer ways to combine results that do not rely on directly comparable score scales. RRF focuses on ranking position, rewarding documents that consistently appear near the top across different retrieval methods. RSF, on the other hand, works directly with raw scores from different sources of relevance, using normalization to minimize outliers and align modalities effectively at a more granular level than rank alone can provide. Both approaches quickly gained traction and have become standard techniques in the market. How did the market react? The industry realized the need to introduce hybrid search capabilities, which brought different challenges for different types of players. For lexical-first search platforms, the main challenge was to add vector search features and implement the bridging logic with their existing keyword search infrastructure. These vendors understood that the true value of hybrid search emerges when both modalities are independently strong, customizable, and tightly integrated. On the other hand, vector-first search platforms faced the challenge of adding lexical search. Implementing lexical search through traditional inverted indexes was often too costly due to storage differences, increased query complexity, and architectural overhead. Many adopted sparse vectors, which represent keyword importance in a way similar to traditional term-frequency methods used in lexical search. Sparse vectors were key for vector-first databases in enabling a fast integration of lexical capabilities without overhauling the core architecture. Hybrid search soon became table stakes and the industry focus shifted toward improving developer efficiency and simplifying integration. This led to a growing trend of vendors building native hybrid search functions directly into their platforms. By offering out-of-the-box support to combine and manage both search types, the delivery of powerful search experiences was accelerated. As hybrid search became the new baseline, more sophisticated re-ranking approaches emerged. Techniques like cross-encoders, learning-to-rank models, and dynamic scoring profiles began to play a larger role, providing systems with additional alternatives to capture nuanced user intent. These methods complement hybrid search by refining the result order based on deeper semantic understanding. What to choose? Lexical-first or vector-first solutions? Top considerations when choosing a hybrid search solution When choosing how to implement hybrid search, your existing infrastructure plays a major role in the decision. For users working within a vector-first database, leveraging their lexical capabilities without rethinking the architecture is often enough. However, if the lexical search requirements are advanced, commonly the optimal solution is served with a traditional lexical search solution coupled with vector search, like MongoDB. Traditional lexical - or lexical-first - search offers greater flexibility and customization for keyword search, and when combined with vectors, provides a more powerful and accurate hybrid search experience. Figure 2. Vector-first vs Lexical-first systems: Hybrid search evaluation. Indexing strategy is another factor to consider. When setting up hybrid search, users can either keep keyword and vector data in separate indexes or combine them into one. Separate indexes give more freedom to tweak each search type, scale them differently, and experiment with scoring. The compromise is higher complexity, with two pipelines to manage and the need to normalize scores. On the other hand, a combined index is easier to manage, avoids duplicate pipelines, and can be faster since both searches run in a single pass. However, it limits flexibility to what the search engine supports and ties the scaling of keyword and vector search together. The decision is mainly a trade-off between control and simplicity. Lexical-first solutions were built around inverted indexes for keyword retrieval, with vector search added later as a separate component. This often results in hybrid setups that use separate indexes. Vector-first platforms were designed for dense vector search from the start, with keyword search added as a supporting feature. These tend to use a single index for both approaches, making them simpler to manage but sometimes offering less mature keyword capabilities. Lastly, a key aspect to take into account is the implementation style. Solutions with hybrid search functions handle the combination of lexical and vector search natively, removing the need for developers to manually implement it. This reduces development complexity, minimizes potential errors, and ensures that result merging and ranking are optimized by default. Built-in function support streamlines the entire implementation, allowing teams to focus on building features rather than managing infrastructure. In general, lexical-first systems tend to offer stronger keyword capabilities and more flexibility in tuning each search type, while vector-first systems provide a simpler, more unified hybrid experience. The right choice depends on whether you prioritize control and mature lexical features or streamlined management with lower operational overhead. How does MongoDB do it? When vector search emerged, MongoDB added vector search indexes to the existing traditional lexical search indexes. With that, MongoDB evolved into a competitive vector database by providing developers with a unified architecture for building modern applications. The result is an enterprise-ready platform that integrates traditional lexical search indexes and vector search indexes into the core database. MongoDB recently released native hybrid search functions to MongoDB Atlas and as part of a public preview for use with MongoDB Community Edition and MongoDB Enterprise Server deployments. This feature is part of MongoDB’s integrated ecosystem, where developers get an out-of-the-box hybrid search experience to enhance the accuracy of application search and RAG use cases. As a result, instead of managing separate systems for different workloads, MongoDB users benefit from a single platform designed to support both operational and AI-driven use cases. As generative AI and modern applications advance, MongoDB gives organizations a flexible, AI-ready foundation that grows with them. Read our blog to learn more about MongoDB’s new Hybrid Search function. Visit the MongoDB AI Learning Hub to learn more about building AI applications with MongoDB.

September 30, 2025