User intent understanding

Applying user intent and the meaning, interpreting complex queries beyond keywords, and ensuring results match the searcher's true intent and context.

Contextual relevance

Employing context and personalization to tailor search results, enhancing relevance and accuracy for each user.

Pre-processing of text

Preparing the text for processing with the help of computational linguistics tools such as tokenization, sentence splitting, morphological analysis, etc.

Finding and classifying concepts

Detecting and classifying mentions of people, things, locations, events, and other pre-specified types of concepts

Unifying the concepts

Identifying relationships between the extracted concepts, and presenting them into a standard format.

Cut through the noise

Eliminating duplicate data and enriching your knowledge base, integrating the extracted knowledge into the database for future use.

Context-based document classification

Prioritising the context, such as the creator of the data, the location where the data is created or modified, the application of the data, and other variables that affect data.

Content-based document classification

Using deep inspection to examine and interpret data to identify personal, sensitive, and confidential information, before determining the appropriate classification label to apply.

User-based document classification

Using the user’s discretion and knowledge for classifying sensitive data, including its creation, editing, reviewing, and dissemination. With this approach to data classification, an individual can assess the sensitivity level of each document.

Information retrieval - understanding intent and context

Obtaining information, often from large databases, which is relevant to a specific query or need. Aligning results with user intent and situational context, beyond mere keywords.

Content recommendation - categorising key information

Suggesting relevant content to users based on their behaviour, preferences, and interaction history.
Categorizing identified elements into predefined groups such as names, places, and dates, turning text into organised data.

Automated data entry

Replicating human actions to perform routine business tasks. While these programs aren't related to hardware robots, they function like regular white-collar workers.

Sentiment analysis enhancement

Combining statistics, NLP, and machine learning to detect and extract subjective content from text. This could include a reviewer's emotions, opinions, or evaluations regarding a specific topic, event, or the actions of a company.

Text identification

Initial cleanup of unstructured content, followed by extraction from various formats like PDFs and videos.

Text Analysis

Utilising NLP techniques to analyse text, identifying key concepts such as people, places, organisations, mentions of dates, amounts, etc.

Extraction and connection mapping

Classifying and clarifying identified entities against a knowledge base to ensure precise meaning. Mapping the connections between identified entities to weave a network of related concepts.

Indexing and storing

Compiling the enriched data into a semantic graph database, making it accessible and analyzable for future queries.

Advanced topic detection techniques

Utilising complex machine learning models to identify and extract topics from large text corpora.
Recognizing patterns and emerging trends within topics over time.

Contextual topic relevance

Examines the sentiment or emotional tone linked with various topics, offering deep insights into public perceptions or attitudes toward those subjects.

Sentiment and emotion analysis

Analysing the sentiment or emotional tone associated with different topics.
Gaining insights into the emotional responses or attitudes towards certain topics.

Customizable topic models

Develops specialised models for specific industries or fields, ensuring that analysis remains pertinent and adaptable to new information and evolving content landscapes.