Introducing Trusted Web Search Search Where the Source and Provenance Matter

The Web Is Changing

The Web of today, and even more so the Web of tomorrow, is fundamentally different from the Web that traditional search engines were built for.

For most of the Web’s history, content was predominantly created by people and consumed by people. Search engines sat in the middle: they crawled human-created pages, ranked them, and helped people decide what to read and trust.

That model is changing from both directions.

On the production side, generative AI has dramatically lowered the cost of creating content. AI-generated and AI-assisted pages can now be produced at enormous scale. As a result, the Web is becoming larger, more repetitive, and increasingly populated by content whose provenance, originality, and reliability may be difficult to determine.

At the same time, the consumption side is changing just as quickly. People increasingly delegate information seeking to AI systems. Search agents, assistants, and reasoning models search the Web, read dozens or hundreds of sources, synthesize what they find, and return an answer, often without the user ever visiting the underlying pages.

In this ecosystem, finding pages that contain relevant words is no longer enough. Search systems increasingly need to answer a more fundamental question: What information should AI agents, and ultimately people, rely on?

That means reasoning not only about relevance, but also about properties such as authority, provenance, primary versus secondary sourcing, evidential support, completeness, and the relationship between a source and the claim being made.

The next generation of Web search is therefore not simply about finding information. It is about building a trustworthy information layer between an increasingly machine-generated Web and the AI systems that increasingly depend on it. And Naget is building this ecosystem.

Authority belongs to a claim, in context.

A company is a strong source for its own official disclosures. Its marketing copy serves a different purpose. A university can publish both an official announcement and an individual researcher’s opinion. Sharing a domain does not make those pages equivalent.

Our authority model considers who is speaking and whether the content presents factual information or subjective interpretation. Where available, information about the organization behind a site helps establish that context. The page itself still matters.

The five tiers make those distinctions explicit:

These tiers describe the kind of authority behind content. They do not certify that every statement is true. Institutions make mistakes, research changes, and experts disagree. A useful search system should preserve those distinctions while leaving room to inspect the evidence.

The model also identifies a separate low-authority category. That is different from a low-quality page: a useful product listing can have little authority for a factual research question. Content-type policies help keep those distinctions consistent.

The Origin of Information Matters Too!

An official statistical release originates information. A careful news report may explain that same release. Both can carry institutional authority, but they play different roles.

For tiers 1–4, Naget Trusted Web Search separately tracks whether a page is a primary source or a secondary or tertiary source. An original study and a systematic review can both be valuable expert material; one reports new findings, while the other synthesizes research. Keeping provenance separate from authority preserves that difference.

Search by authority and content type.

Our native index organizes crawled pages along two dimensions: authority tier and content type. One describes the relationship between a source and its claims. The other describes what kind of content a page contains.

A dataset, an essay, a software package, and a discussion thread are all webpages. Treating them as one undifferentiated category throws away information about how they can help.

Our webpage classifier recognizes 21 labels: 19 content types and two filtering labels. The content types range from articles, news, reference material, documentation, and academic work to code, data, software, interactive tools, and discussions. They also cover how-to guides, recipes, social posts, media, commerce, marketing, navigation, aggregators, and personal pages.

Content type and authority answer different questions, but they inform each other. A discussion thread is a conversation even when an expert participates. A commercial page remains commercial even on a familiar domain. Combining both dimensions makes it possible to ask for the form of evidence a task needs.

Together, they let a search express something much more useful than a topic alone: official documentation, expert research, institutional reporting, or community-reviewed discussions. You can choose a single combination, several combinations, or a broader set of sources.

These categories are part of the index’s structure. A query can access its specified combination of authority tiers and content types directly, giving it a fast path to the relevant parts of the index. That choice shapes the search space before results are ranked.

A developer can start with official documentation and expert technical material. Someone investigating a news story can focus on reporting and data. A researcher can bring community knowledge into the search when practical experience matters. The scope can change as the question develops.