mescales available have not allowed this to be fully tested or implemented, but the infrastructure in-place would allow for this to be completed in the future (with further development). The current iteration is built with staging and enterprise layers with the intention of trafficking new data through the staging layer into the enterprise layer on an incremental/maybe daily basis. Universal usability has been a key consideration for development, it is appreciated that other organisations may have differing source systems. With this in mind the infrastructure is purposefully source system agnostic so that it can be implemented where there are source applications other than LiquidLogic, this has been achieved through standardised database structures, pipelines and visualisation tool. In the tool the AI service ‘entity recognition’ is called to generate entities which are ingested by a network navigator product within Power BI to visualise. The key entities mapped are people, places and products (and these can be filtered by type and confidence score, with a range of 0.8 – 1.0). The dataset includes over two million documents in standard formats from other agencies (e.g., Public Protection Notices, Police Checks, documents submitted to Multi Agency Risk Assessment Conferences). Entities that appear are not coded by risk or protective factors, and there has been no data matching of records. In reference to the tool being cost effective, optimising costs has been a priority area for the project. Cost estimates at Section 4.4.5 (Financial Feasibility) show ongoing costs could be very small (around £10k per year), but significant unknowns mean making an accurate overall estimate is challenging. This is because set-up and ongoing costs depend on resources available and the pos