Coginov applies Semantic Technology and Artificial Intelligence to transform your unstructured data to deliver outstanding business outcomes
Contact Information
1000 rue de Sérigny, suite 300
Longueuil, QC, J4K 5B1
Have any Questions?
514.933.7495
Mail us today
info@coginov.com
We are a dynamic and diverse team
Our team consists of artificial intelligence (AI) scientists, AI engineers, linguists, developers, integrated document management specialists, and customer service and sales professionals. We have extensive experience in automated natural language processing (ANNP), computational linguistics, and semantic technologies.
With such a deep bench of experts, it was a natural fit to combine machine learning (ML) and NLP approaches to develop a platform that offers contextual search of enterprise repositories and enhanced analytics, all to enable more accurate insights and improved business strategy.
We are experts in NLP
Unlike most vendors in the text analytics market, we have two areas of expertise – NLP and ML – and our technology is defined by applying the combination of these powerful approaches for optimal content analysis. Our semantic analysis, based on industry-specific dictionaries, combines with robust machine learning algorithms to identify meaning and present results in context.
We put the "Stacked Model" to work!
Coginov’s AI framework leverages a stacked model methodology (an efficient method in which predictions, generated using various machine learning algorithms, are used as inputs into a second-layer learning algorithm) that combines an associative memory model with three ML approaches, one of which is based on a powerful deep learning neural network. Our ML framework generates contextualization by combining best practices in language models and topological data mining methods to identify semiotic value in unstructured data repositories.
We are an ML-First operation
We apply machine learning to every step of unstructured data processing. This ML-based approach significantly expands the horizons on usable data. Specifically, our ML-first approach transforms unstructured data into human and machine-readable semantic asset content repositories.
We can then create language analysis and modeling from these semantic assets, which are then applied to perform extraction and analysis of named entity models, key concepts, and singular alphanumeric patterns. These ML-generated semantic knowledge representation structures conform to state-of-the-art ontology standards using innovative language models.
Our multilingual AI platform is available in both a service model (SaaS) and a Platform as a Service model (PaaS).
Our multilingual AI platform is available in both SaaS and PaaS models. The proprietary algorithms behind this Platform have been infused with linguistic and cultural specificities.
In the background, “Coginov Analytics” uses proprietary algorithms exploiting semantic analysis to identify the context of concepts and entities.
In-depth document analysis
Powered by our AI framework, Coginov's analytics platform uses automated, state-of-the-art semantic analysis to draw out the lessons and context behind various topics and for all types of documents or information. Our contextual analysis processor:
-Expands the variety of sources (while remaining relevant) so that all topics can be extracted for review, whether they are contained in structured or unstructured data
-Improves auditing of enterprise content repositories and dissemination structures
-Creates centralized access to all feedback channels
-Minimizes "noise" in the signal by excluding irrelevant content
-Enables more comprehensive distribution of key topics through a wider range of sources and more intelligent contextualization
-Expands the variety of sources (while remaining relevant) so that all topics can be extracted for review, whether they are contained in structured or unstructured data
-Improves auditing of enterprise content repositories and dissemination structures
-Creates centralized access to all feedback channels
-Minimizes "noise" in the signal by excluding irrelevant content
-Enables more comprehensive distribution of key topics through a wider range of sources and more intelligent contextualization
Understanding requests
Many EDRM solutions stop at collecting data and monitoring occurrences of specific keywords. The task of determining how to proceed with data analysis and understanding the strategic decisions suggested by the analysis usually falls to the end user, often resulting in analysis paralysis, wasted time and money. Coginov's platform exists to solve this problem and catalyze the analysis of organizations' data to another level. Using NLP in combination with ML, our platform understands the context of a user query and provides an optimal and more profiled response to user questions. In essence, our semantic analysis, using industry-specific dictionaries and correlated by proprietary ML algorithms, creates a powerful way to identify the intent and meaning of user queries and provide targeted responses based on that context. This ensures that each user is quickly connected to the most relevant and useful information assets and resources, many of which would otherwise remain hidden behind a mountain of data.
Beyond basic keyword analysis
Our platform's ability to understand the context of topics is a key differentiator. Informed by the context of user queries, the platform goes far beyond simple keyword analysis and allows the user to instantly draw actionable conclusions. Coginov's analytics platform focuses on accurate, instantaneous analysis and increased business intelligence. Our differentiators include:
-A unique approach to semantic analysis that enables accurate context capture
-A unique combination of quantitative and qualitative data analysis that ensures relevant conclusions and a call to action
-Rich semantic categories that allow the fortunate discovery of unexpected topics
-Seamless extraction of categories to multiple commenting channels, including the ability to automatically process responses to all common mime formats and to database and CMS repositories
-One-time pre-validation of search results ensures that the analysis is of the highest relevance
-A unique approach to semantic analysis that enables accurate context capture
-A unique combination of quantitative and qualitative data analysis that ensures relevant conclusions and a call to action
-Rich semantic categories that allow the fortunate discovery of unexpected topics
-Seamless extraction of categories to multiple commenting channels, including the ability to automatically process responses to all common mime formats and to database and CMS repositories
-One-time pre-validation of search results ensures that the analysis is of the highest relevance
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