IEI runs monitoring campaigns against data feeds to identify personnel updates, product launches, and other events that are “triggers” for updates to one of our customer’s databases using Enlyton, a new enterprise search tool that uses mathematical indexing to match search terms against data sets.
Implementation involves:
- defining what datasets to crawl,
- specifying what data to index,
- and designing a custom operator interface.
It’s easy to create an interface with the API or tools built in to the admin system. A typical display shows targeted events on the left and potentially relevant entries from the database to be updated on the right. There’s also a checkbox showing the relevance of the found information, and a way to proceed to the next event trigger.
The strengths of the software include:
- highly relevant results
- componentized approach
- simple interfaces
- accessible API
- minimal need for technical help
Another nice feature of Enlyton is its machine learning mechanism. According to Enlyton’s co-founder and CEO Chris McKinzie, “If you already have a taxonomy, we can use that to train it to auto categorize. If you start from scratch, you can just build up the taxonomy over time.” Results gradually get more and more accurate because metadata on search results deemed relevant or irrelevant feeds back into the system, making relevant results more granular as time goes on.