With 15+ years of experience in business-critical projects and almost a decade in large-scale data-driven systems based on Neo4j, other NoSQL technologies and streaming process tools, starting this year, LARUS has started a new challenge heading to graph explainable artificial intelligence (XAI). When we use AI to predict the risk of an investment, take lending decisions, provide explanations for high-scoring cases of suspected fraudulent activities or recommend a medical treatment, black-boxed AI is not suited: human experts must be able to validate the truth of AI produced results and gain new insights and graph technologies add the required context for this level of explainability.