Integrating Internal Tools

nima is designed to work seamlessly with your existing infrastructure you can integrate your internal tools directly into nima’s automated workflows and reporting systems via signals this allows you to leverage your proprietary technology for moderation while benefiting from nima's robust rule engine what is a signal? a signal is the specific outcome from any of your internal detection tools this could originate from a machine learning (ml) model a keyword blocklisting technology a spam detection rule engine configuration process to integrate your internal tool, you must first send a detailed description of the semantics of your detection tool to the product and tech team submit semantics provide the product and tech team with a definition of your tool’s output for example, if you have a spam detection tool, you would provide the structure and meaning of its results true 152,152,152,153 unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type unhandled content type system setup once received, the product and tech team will configure the signal within your environment workflow configuration after setup, the signal becomes available in the ai marketplace when building a rule, select internal tool as the provider the list of configured signals will then appear in a dropdown for you to select handling boolean signals nima’s automated detection workflows operate on the concept of probability if your internal tool returns a boolean outcome (0 or 1), you should configure your thresholds as follows 0% probability (false) this will be considered a green threshold match (flag will be skipped) 100% probability (true) this will be considered a red threshold match (policy will be applied automatically) sending signals via the api once your configuration is complete, you can begin sending signals to the our api in the following way { "priority" "severe", "channel" "automated detection", "reason for request" "lorem abuse reason for request", "customerspecific" { "summary" "", "probs" {"mass friending day" 1}, "detectedby" "internal tool" } } the outcome of your internal detection should be specified within the customerspecific json node at the root level of the input body