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Blog
Here’s an overview of our latest blog posts on enterprise search and artificial intelligence.
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AI Trends 2026: From the experimental phase to productivity
01/07/2026
2025 was a year of high expectations: new AI models of all sizes, major reasoning breakthroughs, and growing availability of generative AI across enterprises. Yet the flood of proof-of-concepts also revealed that not every idea holds up in practice. 2026 will therefore not be defined by the next hype, but by a readjustment: What really works? Where is measurable added value created? What can be reliably operated and monitored in daily work? These trends are shaping this phase.
Blog
23.02.2016
Counting counts – arguments for using statistics to process language
In this post I want to go a little deeper into Ludwig Wittgenstein's argument of "meaning is use" (Philosophical Investigations, 1953), and how it can be seen as a philosophical justification for statistical NLP, including machine learning.
Blog
05.02.2016
How search engines can be expanded by means of word families
What makes a good search engine? You type a sequence of letters and the document is searched for all occurrences of this combination. This way you can quickly and easily find certain text passages within a document. For a user who wants to find out more about a particular topic or the use of a particular word, such a basic search functionality would certainly not be enough.
Blog
18.12.2015
New High Quality Search and Linguistics for iFinder5 elastic
IntraFind has long been known for high quality information retrieval. For our new product generation iFinder5 elastic we completely overhauled our core search technology consisting of our Lucene / Elasticsearch Analyzers and our Query Parser. In part 1 of this blog article I talk about advantages for the standard user and how we are able to reduce configuration efforts.
Blog
26.08.2015
Language Identification and Language Chunking
Identifying the language of a given text is a crucial preprocessing step for almost all text analysis methods. It is considered as a solved problem since more than 20 years. Available solutions build on the simple observation that for all languages typical letter sequences (letter n-grams) exist, that occur significantly more frequent in this language than in other languages.
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