• Oil and Gas company had variety of lengthy and complicated contract templates covering certain parts of its business
  • Deviations in particular templates were not obvious and hard to understand for business users
  • Contract complexity made negotiations time consuming generating additional financial expenditure and had negative impact on building goal-oriented relationships with customers


  • Elevate analysed set of contract templates utilising machine learning platform KMStandards, to identify best practice examples of most frequent and least divergent contract clauses
  • After Phase I was completed and approved by business, consultants got into semantic layer of drafted template to change legal language into ‘plain English,’ that is easy to understand and left less room for uncertainty
  • In collaboration with WCC (f/k/a IACCM), ran simplified templates through a visualisation project, replacing and adding graphical descriptions in eligible parts of the contract for further simplification


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Reduced wordcount by 40% in key contracts
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Improved business adoption of contract templates with plain English and visualisations
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Decreased contract negotiations time with fewer amendments
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Less legal team effort required by business team after contract signature

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