As the amount of content online grows larger every day, there has been a concerted effort to develop tools to help users organize and sift through the wealth of information. We’ve already seen ReadCube, which allows users to search multiple PDFs and receive article recommendations, and now Squirro aims to provide researchers with a data filtering platform that offers content based on the way the service is used. Revolving around the central service of a digital research assistant tool that scans the web for content based on keywords, Squirro finds results from webpages, social media sites and private databases and presents them as one feed. Users can control which sources the service trawls when looking for relevant content based on their needs and can also import their own local data – from documents or content management software – to be incorporated into search results. Once the service has been customized to users’ needs, Squirro recommends new content tailored to each user, meaning that the feeds become a source of data that is constantly updated. The site has been developed for both one-off searching and ongoing research projects and users can share their feeds with colleagues or other people searching similar topics, adding a social element that could help build relationships between those in similar industries. In addition, the Digital Fingerprint function tracks when a search is saved, deleted or shared with the community, enabling each account to ‘learn’ how it is being used and tailor content based upon that data. The video below offers more information about the platform: Aimed at those researching for professional purposes – such as journalists or people working on pitches and projects – Squirro is available for a 30-day free trial or USD 9 a month and USD 99 a year for full usage. Filtering online data down to the most relevant material could offer an advantage to companies that rely on niche digital content – one to get involved in?