Space Enhanced Decision-Making in Recruitment

  • ACTIVITYDemonstration Project
  • STATUSCompleted
  • THEMATIC AREAOther

Objectives of the service

Helping organisations to hire top diverse talent that is personalised to the company’s culture for today and tomorrow (futureproof), without bias. MeVitae automates the candidate shortlisting process and learns recruiter’s patterns to foster intelligent decision making, tailored to company’s needs. The solution’s steps are:

  • Matching Algorithm: Scoring, ranking and shortlisting candidate CVs to determine job suitability. This uses past employer data, present (current CV and job spec) and future (via labour market trends). Understanding the labour market enables employers to understand talent marketdemands (e.g. what jobs are in demand within a certain demographic, current wages etc.) for future work trends 
  • Redaction: Using natural language programming (NLP), MeVitae removes all information from a candidate’s CV that could be cause for discrimination, e.g. age, gender, etc. before forwarding scored CV’s to employers
  • Learning: Track employee progress for learning hiring behaviours. Over time, MeVitae becomes smarter. It learns more about the employer as they select candidates from the shortlist, therefore performing better the more it is usedand therefore ensuring that talent fits the company culture.
  • Behind MeVitae’s technology is a breadth of data including the ability to be sensitive to talent market demands.

Users and their needs

Buying power for the solution is located with the Head of Diversity and Inclusion, HR director or at the executive team. However, the solution will be used by recruiters within organisations (UK and U.S. focus). Such organisations are:

  • Medium and large companies with <500 employees or larger than 1000+ employee size (optimal)
  • Recruitment agencies as they often work with medium and large companies, and received high applicant numbers (future plan)

Their user needs are a solution that:

  • Fit seamlessly into the current HR function (i.e. Application Tracking System or ATS) 
  • Needs to be personal to every company and their HR department 
  • Not have any biases (unconscious or algorithmic) 
  • Faster than current processes in reviewing CVs; currently one CV every six seconds 

Service/ system concept

MeVitae is seamless and all results are outputted within a client’s Application Tracking System (ATS). The integration with the ATS is seamless. The two types of information delivered are:

  • Redacted CV that removed biases based on different parameters requires are interested in (e.g. age, gender etc.)
  • Accurate and fast shortlisting of top candidates within ATS

Space Added Value

Using the ESA Patent 572 ‘method and apparatus for monitoring an operational state of system on the basis of telemetry data’, MeVitae will be able to analyse immense amount of unique time-series recruiter patterns data, specific to each recruiter, per job, per firm. This will help develop a unique selling proposition for companies by generating insights, picking up unconscious biases (causes behind lack of diversity in workforce) and predicting real-time trends in the labour market sector. The benefit of the ESA Patent 572 specifically is that it can be used for many parameters, including those MeVitae could potentially miss with little pre- knowledge, and is more efficient than threshold-based techniques. The Patent will be directly incorporated into MeVitae’s solution.

Current Status

MeVitae have successfully completed the project with paying clients.

  • MeVitae can integrate with 20 Application Tracking Systems including new partners such as Lever and FreshTeams
  • Acquiring clients in the UK and U.S. 
  • Build a new partnership program
  • Testing MeVitae with early adopters and gaining feedback from clients
  • Raised a bridge investment round April/May 2021, planning for Series A
  • ISO 270001 assessment achieved 
  • Partnered with technology companies including Oracle and Microsoft

Prime Contractor(s)

Status Date

Updated: 22 November 2021 - Created: 21 August 2020