Glossary

Insight Engine

What is Insight Engine?

An insight engine is a form of enterprise search that brings together information from many sources, such as documents, emails, databases and applications, and analyses it with AI techniques. Rather than mere lists of hits, it delivers prepared answers, connections or recommendations, often in the context of the task at hand. The term was popularised mainly by industry analysts. Example: before a client meeting, a sales team sees all relevant contracts, tickets and notes in one place.

Topics

Enterprise search
Knowledge management
Analytics

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Related terms

AI Search

AI search refers to search methods that use AI to grasp the intent behind a query, weight results and often generate a summarised answer with sources rather than a plain list of links. Technically, they use keyword search, vector search over embeddings or a combination, frequently followed by a language model. Examples include answer engines on the web and internal knowledge search that helps staff find what they need in manuals and policies.

Search
Knowledge
+1

Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

Search
BM25
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Unstructured Data

Unstructured data follow no fixed table schema: emails, contracts, manuals, web pages, presentations, images or audio recordings. They make up a large share of organisational knowledge but were long difficult to analyse automatically. Language models can read and search such content and turn it into structured information, for example extracting deadlines and amounts from a contract into a table.

Data
Documents
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Knowledge Engineering

Knowledge engineering covers capturing, organising and preparing expert knowledge so that IT systems can use it. Originally it focused on rules for expert systems; today the priority is often to document scattered knowledge from manuals, process descriptions and people's heads so that AI agents can answer reliably from it. This involves clear structures, up-to-date sources, defined responsibilities and closing identified gaps.

Knowledge management
Consulting
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Template