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Intelligent Web Discovery

Uncovering relevant information in real-time

Image Description

Conduct in-depth research and stay up-to-date with a specific area of interest just like a human. Using the latest in situationally aware machine-learning techniques, this knowledge function can keep track and discover relevant sources of information; articles, blogs, research, financial documents, tweets, etc. all in real-time. By configuring the tool to focus on a specialized area of expertise it can continuously uncover relevant information and provide you with a domain-focused data feed as accurately as a human.

Key Features

  • No Down-Time -

    Unlike a human researcher, a machine is always switched on. With 99.999% uptime the A.I continuously scours for relevant information

  • Autonomously discovery -

    Once seeded with the relevant search keywords the intelligent web discovery knowledge function repeatedly  discovers relevant sources of information

  • Self-governing awareness -

    Once the knowledge function is seeded the machine will intelligently distill what information is relevant. Over time the tool becomes more intelligent and delivers greater value. 

  • Smart refinement -

    The knowledge function keeps refining the search based on the outputs and user-generated feedback. Over time the tool quickly becomes smarter and the quality of the information surfaced improves massively.

= <text to search>&keyword
= <comma_separated>&sort
= 1|2|3|4&source_url
= <comma separated url>

response : { "total":60, "page":1,"size":10, "totalPages":6,

    "data": [

{ "title": "World Music Awards on Instagram...",

"link": "https://www.instagram.com/...",

"displayLink": "www.instagram.com",

"details": "1 hour ago ... 45 Likes, 0 Comments - World Music Awards (@worldmusicawards) #SelenaGomez chooses ...",

" ... " : " ... ",

"pagemap": { ... }

How It Works

The application starts by converting the keywords into a machine-processable query format. This query is then used by the model to look for relevant information across the web by crawling direct and nested links based on the user-defined keywords. The model is tuned to discover new sources and provide relevant information continuously as the model runs the query continuously in a loop looking for new information in real-time. All unique new relevant information is returned by the function continuously. The model also uses an in-built noise function which learns continuously to reduce non-relevant outputs. The query can be refined further by adding or modifying the keywords based on the results returned by the function.

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