NLWeb Pioneer Q&A: Qdrant

We just announced NLWeb at Microsoft Build—starting with a GitHub repo to help developers explore it, and a short list of enterprise pioneers testing it out in the real world. Qdrant is one of those early innovators shaping where this goes.

Known for their open-source vector database purpose-built for semantic search, Qdrant is helping developers supercharge the intelligence of their search interfaces—without rebuilding their entire stack. By integrating with NLWeb, Qdrant makes it easy to add fast, intent-aware, and context-rich search to websites and apps of any size.

Below, the Qdrant team shares how this integration came together, what developers can expect, and why NLWeb might be the unlock that brings semantic search to the mainstream.

Q1: Why Qdrant Sees NLWeb as a Practical Step Forward for wider Semantic Search Adoption

In conversations with developers in our community, it's clear that conventional search bars no longer meet modern expectations. They're often rigid, keyword-dependent, and blind to user intent. What developers are asking for - across domains - is a way to make search conversational, adaptive, and semantically aware. NLWeb offers a targeted solution: a framework that allows developers to power the search bar with a natural language interface - bringing together semantic understanding, similarity-based retrieval, and relevance-aware ranking without overhauling their existing architecture. It decouples interface concerns from retrieval logic, enabling tighter control over intent resolution and result ranking - all using tools they already work with.

“NLWeb represents precisely the type of advancement our community has been seeking - enabling developers to implement powerful, context-aware search directly within their existing web applications. Collaborating with Microsoft on this protocol lets us put production-grade semantic retrieval into developers’ hands, with integration into their applications possible in minutes. That kind of speed and simplicity is exactly what’s needed to bring modern, intelligent search into the mainstream,” notes André Zayarni, CEO of Qdrant.

Q2: What are the Technical Capabilities Qdrant Provides for NLWeb Integration?

Qdrant is an open-source vector database built to handle semantic similarity search at scale with high precision and performance. Developers rely on Qdrant for its advanced metadata filtering and one-stage filtering - both crucial when relevance and responsiveness through low-latency retrieval are key. In the context of NLWeb, Qdrant makes it easy to operationalize semantic ranking and retrieval logic, even across large datasets, without introducing complexity into the search architecture.

Q3: How Can Developers Integrate Qdrant with NLWeb in Practice?

Our developer community appreciates streamlined workflows that accelerate deployment. With Qdrant's NLWeb integration, developers can quickly deploy local or distributed Qdrant instances and connect them via NLWeb’s dedicated API connectors. Integration is fast and low-overhead. Developers can set up a Qdrant instance, plug it into NLWeb using the available APIs, and begin querying semantic data structures almost immediately. The architecture avoids unnecessary complexity, making it easy to prototype and iterate on real semantic search workflows without deviating from existing tooling or infrastructure.

Q4: What are the use cases for NLWeb and Qdrant

We see clear demand across industries for more flexible, natural language-driven search. In e-commerce, developers are moving beyond predefined filters and static keyword matching toward semantic retrieval that adapts to user intent - improving discovery and reducing friction in the buying journey. On platforms with large content catalogs, from media libraries to knowledge bases, the ability to semantically align user queries with the right document or asset drives significantly better outcomes. We're also seeing interest from teams building customer support tools and internal search, where structured metadata filters combined with real-time vector retrieval can surface contextually relevant answers with fewer steps. NLWeb enables this new layer of interaction at the search interface, while Qdrant provides the infrastructure to execute fast, filtered, and intent-aware search reliably at scale.

“From the perspective of our community, NLWeb provides a focused framework for turning static search bars into intelligent, adaptive semantic interfaces powered by natural language. Qdrant brings the retrieval and ranking infrastructure behind that shift - semantic filtering, relevance ordering, and fast similarity search - all production-ready. Supporting Microsoft on this initiative aligns with what developers tell us they need: practical tools, tight control, fast paths to deploying AI-powered search, and the confidence that comes from building on open-source foundations,” emphasizes André Zayarni.

Qdrant is excited to support the launch of NLWeb and is looking forward to seeing what the developer community will build with it, especially as natural language interfaces become a standard part of modern search experiences.