RadarRadar enhances its data management platform with AI-powered textual reporting
AI-driven reporting has emerged as one of the most popular use cases for artificial intelligence in commodity businesses. It is widely regarded as a low-hanging fruit: relatively low risk because AI systems are typically granted read-only access to data, yet highly valuable because they eliminate the need to build complex reports or write sophisticated queries that require advanced technical skills. As a result, many companies are now exploring ways to access and analyze business data using simple natural language prompts.
This trend extends across the entire software ecosystem, including CTRM vendors, commodity companies with in-house IT teams, consulting firms, and specialized software providers. However, to make AI-based reporting reliable and trustworthy, all these solutions must be built on a solid data foundation. Data quality and data governance are among the most critical success factors for AI initiatives. Many so-called AI hallucinations are the result of poor data quality, use of biased data in training data sets along with other reasons such as rewarding the model with guessing over factual checking, etc. For organizations implementing AI-driven reporting, data consolidation and governance is a critical prerequisite.
This is where RadarRadar has a clear advantage. As a dedicated data management platform, it collects, normalizes, and harmonizes data from multiple sources, including CTRM systems, ERP platforms, market data feeds, and other enterprise applications. Data governance is one of RadarRadar’s core strengths. The data platform is production proven and therefore can provide a robust foundation for deploying enterprise AI applications. The AI-powered textual reporting capabilities of the RadarRadar platform are now a reality and were demonstrated during the company’s recent webinar.
Ray Intelligence – the embedded AI capability within the RadarRadar platform – currently focuses on positions, pricing, and mark-to-market analysis. For existing customers, adopting AI-powered reporting is a seamless process. The AI assistant is available alongside traditional dashboards and automatically appears when enabled in the customer’s environment. There is no need for separate security configurations: the AI functionality inherits existing user roles and access rights, ensuring that data access remains fully aligned with established governance practices.
Further details shared during the webinar included the integration with Azure OpenAI as part of the client infrastructure managed by RadarRadar. Data is processed under enterprise-grade security controls and is not exposed to public AI services. While the large language model (LLM) is used to understand natural language queries and generate responses, data retrieval and calculations remain entirely within the RadarRadar platform. In other words, Ray uses LLM to determine how to find the answer to a user’s question, but LLM itself does not have direct access to the underlying business data.
Alexander Regnault, CTO of RadarRadar, described Ray Intelligence during the webinar as a traceable, auditable, and trusted AI solution operating across the entire RadarRadar data management platform.
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