g with the child over three time periods, there will be three records in for that Child+Worker combination. After all documents and case notes are processed, the expected estimates are:  An estimated 364,417,866 AI entities found in documents – average 177 entities per document  An estimated 263,331,075 AI entities found in case note – average 35 entities per case note For the last two items, entities can be repeated across documents or case notes. If “Dave” is found 10 times in a document, there will be 10 entities in the table. 4.1.4 Progress Against Requirements (MoSCoW) The project used MoSCoW methodology to prioritise requirements, this involved categorising requirements as, ‘must-have’, ‘should-have’, ‘could-have’ and ‘won’t have’. Below is a summary of the twelve items identified as must haves: ‘Must-have’ item Comments Compliance with GDPR/info security - ensuring the confidentiality and privacy of the children's data represented Achieved. See Data Protection Impact Assessment. Reassurance for users about compliance with GDPR was one of the main themes coming out of the initial user feedback sessions (CYPS Locality Events) – because of this feedback a summary was added to the practice guidance document. Search multiple data sources and file types - with one click and immediately relay the most relevant information to the user. The search needs to be able to see unstructured information, structured information and semi-structured information like LiquidLogic forms (e.g., a form that is structured but has free text fields within it, includes children and family assessment) Achieved (partial) - further work needed to finalise how LiquidLogic forms are used within the product. Text limitation was overcome (see issues and resolutions Section 4.1.2). Optical Charact