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xAIgent.

Making Content Findable

Text, Email, HTML... Text of any subject domain, xAIgent turns content of any subject matter into contextually accurate, relevant lists of keyphrase tags in a matter of seconds.

Making Content Findable

Text, HTML, Email... Any Text of any Subject matter domain xAIgent turns content into bullet point accurate Metadata. The most important content highlights - Keyphrases - elevate the importance, ranking and Findability of any unstructured content.

Extracting the most important highlights of any content, metadata, with contextual relevance gives your content of any subject domain prominent Findability.

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Precise Bullet Point Content Summaries

Perfectly suited for Automated Topic Curation, Search Engine Optimization (SEO), Content Management, Knowledge Management, Document Management, Intelligent Search, Indexing, Categorization, Cataloguing, Content Tagging...

xAIgent’s unique, patent backed technology delivers precise bullet point content summaries from any subject domain automatically - without training - without manual 'oversight'.

xAIgent extracts bullet point keyphrase summaries of any subject matter content displaying in perfect context how those keyphrases are topically used in the target content allowing for accurate definition resulting in unparalleled accuracy and subject matter relevance without the need for subject matter experts. Massive volumes of unstructured data can now be Tagged, analyzed and categorized with unbiased, automated accuracy.

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Making Content Findable™

' Some of the many ways xAIgent makes finding the information you need easier... '

Doc-Tags

With Automated Precision Doc-Tags will turn mountains of unstructured content into immediately findable knowledge based upon each document's own unique set of keyphrase Metadata tags.

IndexDocs

The Document Index Generator is the fastest and easiest way to generate a back-of-the-book index for your Word Documents - of any size.

Help Me Study

Help Me Study is a free web application for building fill in the blank questions from study notes, chapter text and supplemental information.

Tenbase2.com

tenbase2 is a large language model company that has specialized in making video easily searchable. Using a collection of AI+ML stragegies including xAIgent, tenbase2 converts audio to text and then extracts the keyphrases [metadata] from the text making those processed videos immediately findable with contextual accuracy and relevance to search criteria.

Frontier Centre for Public Policy

The Frontier Centre for Public Policy uses Doc-Tags to elevate the findability of their published documentation, research and supporting materials. An early adopter of Doc-Tags, Frontier Centre for Public Policy makes important but complicated concepts understandable and findable for all.

CaseText

Aggregating new case law by the day, every day across 50 states is a large task. Making all of that case law findable and with relevance an even bigger challenge. Early on, the patent-backed xAIgent process made CaseText's challenge just that much easier, with contextual accuracy and unbiased relevance.

Frequently Asked Questions

Tags are referred to as metadata - metadata helps describe an item: a document, webpage, image, picture... allowing that item to be found in context during a search process. Tags can be Keywords, Key Phrases or a Term. Tags are concise descriptions that provide context when describing the primary subject matter of that content. Tags are added to the profile of the content file; for Web pages they are known as Meta Tags, for Social Bookmarking, Word Documents and other common file types - Tags are added to the Tag Property.

The xAIgent currently works with monolingual documents in English, French, Japanese, German, Spanish, or Korean.

For some applications, you may wish to have a list of keyphrases that covers a whole collection of documents, where each document has been processed individually by the xAIgent. If you have no constraints on the size of the list of keyphrases, you might simply take the union of all of the phrases as your combined list. To reduce the size of the list slightly, you might drop words that have the same stem (e.g., "automobile" and "automobiles"). If you want to substantially reduce the size of the list, then you can assign a normalized score to each keyphrase and select the keyphrases with the highest normalized scores. Doc-Tags is a really good commercial example of managing collections of documents and their extracted keyphrases

Automatic document classification (Taxonomy) is the use of software to sort documents into various pre-defined categories. A similar task is automatic document clustering, in which there are no pre-defined categories, so the software must create the categories by itself. If you wish to use the xAIgent to generate feature vectors, we suggest the following approach: Apply the xAIgent to all of the documents in your sample collection. Take the union of all of the extracted keyphrases as the feature set. For each document and each feature, let the value of the feature be the number of times that the given phrase occurs in the given document (regardless of whether the xAIgent extracted it from the given document). Apply your favourite machine learning algorithm (e.g., decision tree induction, neural network, genetic algorithm, etc.) to the resulting feature vectors. If you want to learn more about automatic document classification and clustering, there is a hypertext Bibliography on Machine Learning Applied to Text http://www.researchgate.net/publication. xAIgent can be used to generate features for use in feature vectors for machine learning algorithms.
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