Can the impact of justice processes be enhanced with the inclusion of a heterogeneous component into an existing cost–benefit analysis (CBA) APP that demonstrates how benefactors and beneficiaries are affected? Such a component requires: (i) moving beyond the traditional cost benefit conceptual framework of utilising averages; (ii) identification of social group or population-specific variation; (iii) identification of how justice processes differ across groups/populations; (iv) distribution of costs and benefits according to the identified variations; and (v) utilisation of empirically informed statistical techniques to gain new insights from data and maximise impact to beneficiaries. In this paper, we outline a method for capturing heterogeneity. We test our method and the CBA online APP we developed using primary data collected from a developmental crime prevention intervention in Australia. We identify how subgroups in the intervention display different behavioural adjustments across the reference period revealing the heterogeneous distribution of costs and benefits. Finally, we discuss the next version of the CBA APP, which incorporates an AI-driven component that reintegrates individual CBA projects using machine learning and other modern data science techniques. We argue that the APP, enhances CBA, development outcomes, and policy making efficiency for optimal prioritization of criminal justice resources. Further, the APP advances policy accessibility of enhanced, social group-specific data illuminating policy orientation for more inclusive, just, and resilient societal outcomes.