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TechnologyOrganizationTool/project (Click to open) 👆Country of OriginTarget group/intended userCrime PhaseWebsiteDescription

Access Data Group

Law enforcement

FTK is a database driven solution that processes and indexes large amounts of data up-front to allow efficient filtering and searching. Users interact with FTK via a web interface which allows them to conduct computer forensic investigations. The product provides file filtering, search functionality and access to remote systems on the network as well as helps law enforcement, corporate security and IT professionals locate, analyze and evaluate the evidentiary value of files, folders and computers

Child Rescue Coalition

Law enforcement

Automating the handling of evidence –including chat logs, data and videos – for live streaming abuse cases. Because mobile applications are often used to share CSAM and groom children, this solution will also help collect data against suspects with a sexual interest in children.

Eckerd Connect

Social services

Using a predictive analytics model that builds assessments after amassing historical data, Eckerd used RSF to identify the children who could only be returned to their parents with a heightened level of scrutiny by caseworkers.


Law enforcement

Analyze Collaboration Server is a central repository for storing and sharing information. Analyze Collaboration Server Enterprise enables organizations to store and access all the relevant material they have ever collected. The software facilitates image and video processing via a custom user interface, allowing investigators to work with a central store of case data. The Griffeye AI is made up of two core solutions: The CSA classifier, which has been trained on real CSA case data, and the Object classifier, which can detect various objects and details in images.


Law enforcement

YOSE is an AI-driven search engine that lets you instantly track down intelligence within any file type—even from the largest, most unstructured stockpiles of locally stored data.


Law enforcement

PredPol uses a machine-learning algorithm to calculate its predictions on where crimes will occur. Historical event datasets are used to train the algorithm for each new city (ideally 2 to 5 years of data). It then updates the algorithm each day with new events as they are received from the department. This information comes from the agency’s records management system (RMS). PredPol uses ONLY 3 data points – crime type, crime location, and crime date/time – to create its predictions. No personally identifiable information is ever used. No demographic, ethnic or socio-economic information is ever used. This eliminates the possibility for privacy or civil rights violations seen with other intelligence-led policing models.