Pioneering AI for Requirements Quality and Compliance

Keeping track of all your requirements and helpdesk cases is challenging. eXgence extracts embedded intelligence from text, and supercharges product lifecycles


If you have a database or project requirements,  helpdesk cases or test standards, from multiple contributors and stakeholders, it can be hard to make sense of it all.   Project costs grow exponentially with complexity, because it takes more management to avoid duplication and inconsistency, and gets harder to keep track of the big picture.   

eXgence Solution

eXgence uses both machine learning and semantic models to process your data and find text with similar meaning.  Similarity ratings are then used to categorise text, build logical groupings, and trace content throughout a project life-cycle.  When new information is introduced, it's easy to find related information that's gone before, and avoid re-inventing the wheel.  


Automatic analysis of project content can deliver  substantial time savings and massive efficiency gains.  By extracting the embedded intelligence in the data, the project runs more smoothly, users get a faster response from the project team, and the project is insulated from changes in key personnel.


When you have a new requirement, or new helpdesk case, eXgence can find similar examples in the existing data, with astonishing accuracy.  Works way better than any search engine of knowledge base capability.


Run eXgence over your existing database and identify groups of items with the same essential meaning.  Identify the most common causes of helpdesk issues, or group randomly gathered requirements into related topics

Traced Links

Find inter-relationships between project documents such as Requirements, Functional Specs, and Test Casts.  Or find quality shortfalls, where such relationships are missing.    

Key Features

Automated Analysis. Process natural text to extract structured information and meaning.

Text Similarity Scoring.  Creates scores to rate the similarity of text statements in your data, according to their meaning.   This goes a long way further than simple keyword matching, or even best-in-breed machine learning implementations.

Automatic Categorisation. Able to process large numbers of statements in order to find logical categories by meaning.   In a project context, can be used to find all requirements about the same topic, or in a helpdesk context, to find the most common causes of customer support issues. 

Automatic Matching.  When a new requirement or helpdesk case is introduced, automatically find similar examples that have gone before.  

Automatic Traceability.   Find relationships between documents and content from different stages in the project lifecycle, for example which test cases should be used in relation to a given business requirement, or what are the original business requirements relating to a functional specification. 

Key Benefits

Intelligence.  With large datasets and multiple contributors its impossible to keep track of everything. eXgence Identification pre-existing similar entries within your requirements or helpdesk data reveals knowledge you didn't know you had.

Time Saving.  Rather than reading through large numbers of entries in your requirements or helpdesk database, eXgence can identify the similarity in a fraction of the time.

Streamlines Testing.   New requirements or bug fixes often give rise to new test cases.   Running the appropriate test cases often duplicates or supersedes test cases that have done before.  eXgence streamlines the process by identifying similarity. 

Improves Quality.  Our eXgence traceability lets you spot requirements which lack test cases, deal with that issue, and drive up quality. 

Faster Development.  The complexity that comes from large projects makes it difficult to respond to new requirements because it's almost hard to figure out the impact on existing capabilities.   eXgence processes large amounts of text rapidly, identifying relevant points.  


The work has been carried out at the University of Manchester on a grant from the Engineering and Physical Sciences Research Council. The University of Manchester Intellectual Property has provided support during the commercialisation process.


In the first stages of development we have worked with companies such as Atos, Fujitsu, and NASA to validate our technology. Currently, we are actively seeking input and engaging with other companies to find ways in which eXgence technology will add value to their offerings.


2013 Winner Best Student Tool @ 28th IEEE/ACM International Conference on Automated Software Engineering
2014 Winner @ IQuBit Digital Accelerator
2015 Winner 1st prize Research Category @Venture Further

Executive Team

Dr. Erol-Valeriu Chioasca

Managing Director

Erol designed and implemented the requirements repository technology during his PhD in Software Engineering.

Harry Manley


Harry is a successful software-industry entrepreneur currently providing commercial guidance to the eXgence team.

Wai Lau


Wai developed ground breaking AI software that analyses large amounts of raw data in order to improve companies' systems and processes.

Scientific Advisory Board

Dr. Keletso Joel Letsholo

Joel is an expert in requirements engineering, specifically in information mapping algorithms and modelling languages. 

Dr. Liping Zhao

Liping is an expert in software requirements patterns and software design with more than 20 years of experience in software systems R&D.


March 2016
1st-stage Prototype. Exgence has released its first-stage prototype, illustrating how semantic analysis can be applied to text statements

June 2016

Exgence partners with Matrix, and Inflectra.   Exgence has entered into partnerships with two vendors in the requirements analysis space.  Matrix, a company specialising in requirements management for medical devices, and Inflectra, a US based company focussing on the software lifecycle. 

November 2016

Exgence is delighted to announce it has agreed an Angel funding round with a group of three Manchester-based investors. This funding will allow us to build out our proposition, deliver integration with more ALM and Requirements Management Systems, and prove our concept in a commercial context.

February 2017

Exgence has released its second-stage prototype.   The new prototype processes databases of requirements, identifying inter-relationships.  Contact us for a demo. 


Matrix Requirements

Oberkirch, Germany

Matrix Medical Requirements develop a leading software solution for Requirements Management in the Medical Devices industry.

Inflectra Corporation

Maryland, USA

Inflectra Corp. develop SpiraTeam, a leading ALM solution used extensively by software development teams around the world.

Enterprise Virtual Analyst

EVA is a cloud based implementation of the natural language processing engine at the heart of our solution.  It was built as a proof of concept to illustrate our capabilities and how we are developing our solution towards commercial readiness.   It can

  • flexibly Import text-based requirement statements
  • Process the body of text to find inter-relationships
  • Display matched pairs of requirements with ranked scores indicating similarity according to machine learning and semantic analysis
  • Use the pairs to create groupings
  • Process incremental requirements and find matches in the existing database

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