When content is uploaded or created in the platform, the Staffbase algorithm analyzes it and extracts key elements, such as the title, text, and metadata. These elements are then stored in a structured index that helps the search engine deliver fast and accurate results.

When content is indexed, Staffbase applies the following strategies to ensure users can easily find answers, even if they don’t use the exact wording. For example, when searching for the title "International Volunteer Day":

• Phrase Search Indexing
  Multi-word phrases (usually 2–3 words) are indexed together so they can be matched as a unit during a search.
  Example: "International Volunteer Day", "Volunteer Day", "International Volunteer"
• Full Word Search Indexing
  Each word is indexed in its full form, allowing exact matches during a search.
  Example: "International", "Volunteer", “Day"
• Prefix Search Indexing
  Words are broken into multiple prefixes (from 1 to 20 characters), allowing matches based on the beginning of a word.
  Example: For "International": "I", "In", "Int", "Inte", "Inter", … up to "International" (1–20 characters)

This layered approach ensures that users receive relevant results whether they search for the full phrase, a single word, or even just the beginning of a term.

Staffbase indexes a wide range of content types: Pages, News, Files, Users, Apps & Links, Plugins, and Comments.

		Pages	News	Files	Users	Apps & Links	Plugins	Comments
Title	Phrase							-
	Full word							-
	Prefix							-
Content	Phrase				User fields
	Full word				User fields
	Prefix
Metadata	-	Description	Teaser text	-	-	Description	-	-
	Phrase							-
	Full word							-
	Prefix							-
Additional Fields	-				-	-	-	-

Title: The name given to a page, a news, or a file.

Content: The substance of a page, a news, or a file.

Metadata: Information that is not part of the core of a news or a page. For example, the teaser text for a news and the description field for pages

Description: Additional context about a page. The description is available for Pages only and shows in the search results.

Teaser Text: The purpose and aim of the news. The teaser text is available for News only.
Full word: Refers to exact word matching. Example: If users search for “Staffbase”, the search results show content that matches the word “Staffbase” exactly.

Phrase: Multi-word search terms. Example: If users search for “Staffbase Studio”, the results show content that matches the exact combination of both words. 

Prefix: The prefix word matching. Example: If users search for “Staff”, the search results show content that matches the prefix “Staff”, such as “Staffbase”.

User Fields: The profile fields (system and custom) created by an admin in the Studio settings.

Additional Fields: Other structured data that supports search, such as hashtags used in pages or news posts. 

During the indexing, data is processed to make it easier to retrieve information later.

When indexing, the search analyses text in the following ways:

• Case insensitivity: All characters in the content are considered lowercase. For example, apple and Apple are considered the same.
• ASCII characters: The indexing converts alphabetic, numeric, and symbolic characters that are not in the Basic Latin Unicode block (first 127 ASCII characters) to their ASCII equivalent, if one exists. For example, the indexing process changes à to a.
• Language analyzer: The language analyzers are applied to content and teaser text within News and content and description within Pages. The aim is to analyze specific language texts. The analyzers handle the following using the language context:
  • Stop words: Stop words are a commonly used word in a language that is typically ignored in search queries or text analysis because it is considered to be of little value in representing the meaning of a sentence. These words are often short and occur frequently in a language but only carry a little specific information about the content, such as “the”," "and," "is," "in," "of," and "to".
  • Stemming: Stemming removes suffixes from words to obtain a common linguistic base. This helps to group variations of a word, reduce the dimensionality of the data, and improve the efficiency of text processing and analysis.
• Special characters: The special characters, such as !"#$%&'()*+,-./:;<=>?@[]^_`{|}~§° are replaced with an empty space.

Ranking used to rely on keyword matching alone. Staffbase uses two techniques together for every search:

• Keyword matching looks for the exact words and phrases you typed, using the same phrase, word, and prefix matching described above.
• Semantic understanding uses Staffbase's AI to recognize the meaning and intent behind a search, so relevant content can surface even when it doesn't contain the exact words typed.

Both techniques run on every search, and their results are blended into a single ranked list to create a hybrid search.

Within the keyword side of ranking, an exact phrase match counts for more than a single matching word, which counts for more than a partial (prefix) match and a match in a prominent field like a title counts for more than one buried in the body text. 

Staffbase also boosts content based on freshness and access:

• Freshness Boost — Recently published or updated content ranks higher. Recently published or updated content ranks higher. News posts keep their full boost for a month after publication, then it fades gradually. Pages keep their boost for a year after the last edit, then it fades. Old content is not pushed out of search results, but is ranked lower. Other content types do not receive a freshness boost.

Results are ranked with the most relevant ones at the top. On the various content It is also possible to use the dropdown menus for sorting your search results, for example, by date or alphabetical.