AI Site Search 'Unisearch' Deployed on Kenis Corporation's 'Rikanavi' and 'Rikanavi for School'. Significantly Improving Search Accuracy.
NQ Score
80/100
N1 Content Completeness
9
AI Summary (NQ-processed)
On April 30, 2026, Universal Knowledge Inc. announced the implementation of its AI search engine 'Unisearch' on Kenis Corporation's e-commerce sites, 'Rikanavi' and 'Rikanavi for School', optimizing search for massive inventories.
AI Analysis
Frequently Asked Questions
- Q: What are the specific benefits of introducing 'UniSearch'?
- A: In educational and research settings, 'UniSearch' enables the fastest search for the desired experimental equipment or research instruments from a vast product range. By addressing variations in notation and suggesting related keywords, it reduces the burden of procurement tasks and provides a comfortable search environment.
- Q: What kind of company is Kenis Corporation?
- A: Kenis Corporation is a specialized trading company founded in 1947, with the philosophy of 'prospering with science.' It handles approximately 200,000 scientific instruments and boasts top-class market share in the industry, serving educational institutions and research facilities.
- Q: What is the difference between 'Rikanabi' and 'Rikanabi for School'?
- A: 'Rikanabi' is an information and search site for researchers and professionals, while 'Rikanabi for School' is a specialized ordering site for schools and educational institutions, allowing searches by practical categories such as grade level and subject.
- Q: How does UniSearch address notation variations?
- A: UniSearch flexibly absorbs differences in terms like 'Erlenmeyer flask' and 'Meyer,' variations in kanji and kana notation, differences in full-width and half-width alphanumeric characters, and the presence or absence of hyphens in model numbers, preventing zero search results.
- Q: What is the 'Related Keywords' feature?
- A: The 'Related Keywords' feature predicts and suggests narrowing options based on the user's search intent. For example, searching for 'beaker' immediately prompts narrowing options by material and purpose, allowing users to smoothly identify the specific item they need.