Search and knowledge

A generated summary shown above search results, which can answer a question without a visit and changes what a ranking is worth.

A product that replies with a written answer and its sources instead of a list of links, now a category of its own.

Writing so that answer engines can extract a direct answer from your page, which favours plain statements over brand language.

Cutting long documents into passages small enough to search and feed to a model, which quietly decides how good the answers will be.

Getting your documents into a system so they can be searched, including the connectors, permissions and refresh schedule that keeps them current.

Reading a file's real structure, its headings, tables and columns, so that meaning survives instead of collapsing into a wall of text.

A way of representing text (or images) as a long list of numbers so that similar meanings end up numerically close together.

Search across every system an organisation uses at once, returning cited answers and showing each person only what their access allows.

Making content likely to be summarised and cited by generative systems, where being the source of an answer matters more than being clicked.

Combining keyword matching with meaning-based search, so exact terms such as part numbers still land while related wording is also found.

Matching the words as typed, which is precise for names and codes and unhelpful when the reader phrases things differently.

The defined set of documents an assistant is allowed to answer from: policies, manuals, past tickets, whatever you have chosen to load.

A map of how things relate to each other, such as which customer owns which contract, letting a system answer joined-up questions.

A proposed file at a site's root that points AI systems at its most useful content, an emerging convention rather than a settled standard.

The facts about a document rather than its contents, such as owner, date and department, used to filter results and enforce access.

A citation pointing at the exact passage an answer came from, rather than the whole document, so a claim can be checked quickly.

Search that respects who is allowed to see what, so the same question returns different results for different people.

Before answering, the system searches a chosen set of documents, then writes its reply from what it found.

The full path from your documents to a grounded answer, covering ingestion, chunking, search and what is finally handed to the model.

The prepared copy of your content that search actually reads, which is why newly added documents take a while to become findable.

Search that matches on meaning rather than on the exact words typed, so a query about annual leave finds the holiday policy.

Machine-readable markup describing what a page contains, which is how a search or answer engine knows a page holds a product, a FAQ or a definition.

A database built to store embeddings and find the closest matches quickly, even across millions of documents.