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Our knowledge extraction technology works just like a human

Explainable AI


Deep technology extracts, reads and links information delivering truly unique insight

Xapien: How it works
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Our knowledge extraction technology

Our technology works just like a human would do, reading, digesting and learning, but at unparalleled speed and scale.

Fluenci’s natural language processing and machine learning algorithms identify not just the knowledge in any piece of text, but importantly how it inter-relates.

This enables us to stitch together a unique understanding of your subject and the people, companies, events and concepts that relate to them. We tie together oblique references to your subject and the topics relating to them, saving you hours of wading through search results and websites.

Xapien Reference Resolution

Reference resolution

Websites and news articles often only mention the full name of a person or company once. Thereafter pronouns (he/she/it) or descriptors ‘the billionaire businesswoman’ are used to reference them. Our machine learning models identify and tie together these references, enabling us to identify whether risk or facts tied to ‘her’ are actually about your subject or not.

Xapien Relationship Extraction

Relationship extraction

Natural Language Processing algorithms join facts to people and companies, identifying how they participate in events, where those events took place and other people or companies involved in them. Whether it’s a job role, investment, takeover or marriage, we set everything in context to provide you the richest understanding.

Xapien Linking and Enrichment

Linking and enrichment

As humans, we know a huge amount about the world around us. We use this to inform and decipher ambiguity in what we read without thinking. Machines have limited real-world knowledge, so we have taught our software how to find supporting information on the web, allowing us to enrich everything and set it in real-world context.


Our disambiguation engine

Names are not unique identifiers. Whether you are reading text or reviewing corporate records, it is hard to identify whether a mention of a person or company is the same as the one you are looking for.

This is rarely a simple decision based on the name alone. As humans, we look at many factors surrounding the mention. We look at other people, sectors, topics or organisations mentioned in nearby context. We then use this information along with what we already know to form a judgement as to how likely it is to be about our subject.

We call this disambiguation. It’s a critical task when researching and incorrect decisions or assumptions can have huge consequences.

Our proprietary technology models every piece of information as ‘possibly’ true, capturing where it came from and our confidence in it. Our machine learning algorithms, working on top of complex networks of probabilistic modelling then resolve the most consistent view of the data.

This means the information we show you in our reports is harmonious, traceable and we have a high confidence that it is about your subject, not someone else.



XapienFacial Recognition

Facial matching

Faces are one of the strongest indicators that two mentions across two webpages are indeed the same person, whether that’s a biography on a team page, a social profile or a photo of them in the news.

Our technology identifies every face in every image on every page and article we process. Fast neural networks extract the key features and match them across every other facial feature we have.

Xapien Global Name and Address Matching

Global address matching

Address data is one of the most inconsistently presented data types, but being able to match addresses can be critical.

We have replicated this at global scale. Our technology uses vast geospatial data sets and machine inference to resolve locations, in any script, anywhere in the world.
Using population and size data about each location and locality, we are able to determine how significant that location is for matching upon.

Xapien AI graph matching

AI-graph matching

When deciding if two people or companies are the same, we consider all the information surrounding them. This looks a bit like a solar-system of stars and linkages, known mathematically as a ‘graph’.

We use large scale parallel graph algorithms including machine learning techniques to resolve identities within our solar-system of facts and knowledge, making an incredibly complicated task as simple as clicking ‘go’.