Case study

Delivering valuable information to those who can make a difference, helping lead truly ground-breaking and transformative campaigns.

Founded in 2006, STOP THE TRAFFIK (STT) is a pioneer in human trafficking prevention and disruption. Collecting, analysing and utilising data, STT are building a rich picture of trends, hotspots, routes and networks of human trafficking on a global scale. With greater intelligence of the key components of human trafficking efforts, they are generating targeted action to disrupt networks at their source. 

An intelligence-led approach requires a sophisticated method for reporting suspected cases of human trafficking coupled with the underpinning technology to properly harvest, sort and utilise resulting data sets. 

Upon engaging with STT, Netcompany immediately began investigating their historically manual processes with a view to migrate their STOP APP back end architecture from IBM to AWS. By consolidating STT’s digital estate and leveraging AWS’ many tools to automate processes, all submitted trafficking information is now automatically triaged using an algorithmic method, helping to more effectively showcase relevant information. In addition, AI tools were developed to allow articles to be automatically scraped, processed and archived within a single second – something which would routinely take a volunteer several minutes to complete manually. 

Adjacent to the AWS migration, a critical breakthrough for the success of the project was the redevelopment of STT’s anonymous reporting tool – the STOP APP.  With a refinement in the user experience, suspecting onlookers (users) can now quickly submit reports and substantiate these with evidence by attaching photos or video footage, helping STT build more robust data sets and improve their knowledge of human trafficking efforts globally.

Results

• Revolutionised existing systems to more efficiently harvest and process case submission data, reducing the duration from multiple minutes to a single second
• Automated a previously manual and resource dependent process, freeing up volunteers whilst harvesting a greater amount of data automatically
• Implemented machine learning AI tooling
• Consolidated STT’s back end STOP APP estate onto AWS
• Safely processed and triaged over 35 reports of trafficking activity since partnership started