Use NLP to interpret, understand, and generate human language, transforming data into actionable insights

Crafting AI-powered tools that understand human language and act on it,
from search engines that "get" you, to 24/7 chatbots that cater to your every need,
and content strategies that make your online presence felt

NLP Technologies Deployment

We process textual data to extract and structure information, making its meaning easily findable.

Our solutions bring intelligence to your language data, enabling more nuanced analysis and understanding of content and context.

Outcome - Better interpretation of text, making your language data simple and accessible.

Semantic Search

Semantics refers to the philosophical study of meaning

Semantic search is a data searching technique that uses the intent and contextual meaning behind a search query to deliver more relevant results.

We encode the meaning of your text and match it to the meaning of your query. Semantic Search goes beyond traditional keyword matching in search engines. It understands the intent and context behind a user's search query, not just the specific words used.

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.

Semantic Matching

Going beyond keywords to match concepts, utilising vector search and machine learning for deeper query comprehension.

Interoperable Formats

Using formal semantics and interoperable formats for universal data understanding, facilitating cross-system exchanges.

Information Extraction

Transforming an unstructured text or a collection of texts into sets of facts (formal, machine-readable statements)

Automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources to enable finding entities as well as classifying and storing them in a database. 

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.

Automated Dcument Classification

Applying machine learning or other technologies to automatically classify documents results in faster, scalable, and more objective classification.

Document classification can be achieved through three fundamental techniques:

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.

Named Entity Recognition (NER)

Transforming text into structured, actionable insights by identifying key elements

Identifying and classifying important elements in text, like names and places, into specific categories. It highlights key information such as people, locations, organisations, and dates, making text data more structured and understandable.

NER's versatility supports various sectors, improving processes like information retrieval and content recommendation.

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.

Semantic Text Annotation

Tagging documents with relevant concepts

Enriching text documents and unstructured content with metadata that details relevant concepts such as people, places, organisations, and more. This process makes documents machine-readable, allowing them to be easily located, understood, merged, and repurposed.

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.

Topic Analysis (Modelling & Classification)

Topic Modelling simplifies and organises large volumes of text by uncovering prevalent themes and subjects, aiding in content categorization and summary. This process is especially beneficial for extracting and analysing major ideas or trends from extensive text collections, like news articles or research papers.

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.

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Give unstructured data meaning, from plan to implementation.

Since each project is unique, we approach it with an open mind. We customise our solutions to meet your business needs.

Step 1


We discover your business goals and product vision, assess essential features, and map out the project timeline.

Step 2


We outline a clear, detailed proposal, tailored to your needs and present our solution. Through open dialogue and negotiation, we reach an agreement and sign off on the partnership.

Step 3


We execute the offer into the final solution, developing and refining through iterative feedback and rigorous testing to ensure it meets every aspect of the initial proposal.

Step 4


We deliver the complete solution, ensuring a seamless transition, integration into your existing systems, and ongoing support.

Case Studies

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