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Semantic AI


Artificial Intelligence for knowledge management


80% of your business data exist in text format. Less than 1% used for making professional decisions.


of your

business data

are unstructured (Van der Linden, P., 2018)



unstructured data

analyzed for professional decisions (DalleMulle L. & Davenport T, 2017)


What are the benefits of this?


Extract knowledge

Connect knowledge

Create knowledge graph

Quantify knowledge

Contact us:

Use Cases

Use cases

Brand Monitoring

Pharmaceutical Assistance: Drugs-Pathologies Patterns

Document Management in Legal Research

Help to Clinical Practice

Fraud Content Detection

Patient Experience: Extended Clinical Record

MMG Technology for your Project

MMG Technology

Knowledge Graphs

Documents’ internal knowledge combines and connects with other data sources in a single Knowledge Graph. This representation allows to flexibly connect entities of different nature (users, products, departments, concepts) for an integral vision of all their information. The study of the Knowledge Graph with Semantic AI allows us to find deep patterns in the data that are not detectable with other traditional techniques.

Semantic Search

MMG Semantic AI automatically reads, understands and structures your company's documents’ corpus to provide a search engine for high-precision answering to professional queries.

Automatic Summary

MMG Semantic AI generates automatic summaries, understanding and highlighting the most relevant data within a document or set of documents. The generation of summaries can also be conditioned to a user query. In a more sophisticated approach, MMG Semantic AI responds to a user query with a single answer based on the knowledge of multiple documents.

Documentary Management

MMG Knowledge Graph and Semantic AI automatically organize your document corpus to find relevant content, similar cases or connections based on similarities between documents. Our technology finds deep relationships between documents, not only on the basis of direct and evident similarities. Documents can be related, even if they do not share concepts directly. Our technology analyzes more subtle structural aspects such as third party relationships and shadow concepts that allow us to find similarities based on the semantics and context of a document. Patterns emerge from the combination of the whole documents corpus, generating new knowledge.

Linguistic fingerprint

Our exclusive approach, based on forensic linguistics, characterizes texts not only by their content, but also by their form and style that allow us to develop useful predictive models for your business, such as recommendation systems or brand position monitoring.

Ontologies for your domain

For a deep understanding of the text, we use ontologies that describe concepts and their relationships in the context of a sector, department or company. Our Axones technology automatically builds an ontology from formal dictionaries and documents to determine concepts and their hierarchical and horizontal relationships. Axones also enriches pre-existing Ontologies for adapting to a context.

Want to know more?

Download our Whitepapers