AI machine learning on the intranet
Artificial Intelligence Makes Your Intranet Even Smarter
Simplify and automate internal processes with AI.
AI-based intranet solutions
The Final Stage of Automation
With machine learning methods, internal processes can be partially or even completely automated. Building on the widely available amounts of data within the narvika intranet, there are various options for deeper optimisation and automation using AI. This step is usually only advisable when classic rule-based procedures have been exhausted or processing large amounts of data is no longer practical.
According to this principle, we have the narvika offers designed: Start with solid craftsmanship at an affordable fixed price offer including sophisticated rule-based procedures, and already realise significant time savings and efficiency increases in your company. If necessary, expand individual processes or functions with self-learning modules.
AI-based search engine
Find even more relevant
The latest version of the integrator search engine integrated into the narvika Enterprise combines the classic keyword-based search principles with machine learning processes, this is in order to manage large amounts of data even more precisely and to enable the best search results. The search intent of the user can be recognised even more precisely in the case of generic queries and the hits can be displayed more relevantly.
Example: The term "bank" can mean very different things. There is the park bench as well as credit institutions. These two meanings compete with a pure keyword-based search and lead to imprecise search results. With the additional cognitive approach, the classification into areas of importance can be further restricted and in this way even more targeted, thus more relevant results can be delivered.
narvika self-learning search
AI-Search: More features and use cases
You are looking for a specific term, but the document you want only describes that word or combination of words. Exact keyword-based search engines do not provide any results here. A new approach to solving this problem is the use of machine learning methods. Here, a trained computer independently classifies and categorizes content and data and locates them within a multidimensional vector space. If you search for a term, the search returns not only the exact hits but also the hits that are relevant to the content - regardless of whether the term appears in it or not. The exact match is supplemented by a conceptual search.
Adding keywords to images manually is a tiresome endeavor and is often neglected in the hectic everyday working life. Here, too, methods based on machine learning can help and automatically recognize the content of the image. With this knowledge, appropriate keywords can be automated or the next process step can be initiated immediately. Images are classified and categorized here based on pure image information. Dermatologists are already using similar methods for the early detection of skin cancer, but practical examples are also available in a corporate context:
- Spare parts detection: The defective part is photographed and automatically compared with the spare parts database. The corresponding reorder can be suggested or triggered directly
- Damage management: Images of damaged items, packages, or with incomplete references can be automatically pre-sorted and categorized.
- Similarity search: Recommendation function often used in e-commerce for similar products or services based on photos.
With machine learning methods, the computer recognizes the actual content of the document and classifies it accordingly based on previous experience. This is done separately from the individual words in the text and the vector mathematics behind the search results in additional application scenarios such as the comprehensive search in foreign-language texts without their translation.
