Transform natural language into high-performance analytical expressions and complex data scripts instantly.
Formula AI, integrated within the TIBCO Spotfire environment, represents a significant shift in self-service analytics architecture for 2026. Leveraging a specialized Large Language Model (LLM) framework fine-tuned on the proprietary Spotfire Expression Language and IronPython/R scripts, it enables users to generate complex analytical logic via natural language prompts. Technically, it bridges the gap between semantic business intent and execution-ready code, reducing dependency on data engineering teams for complex data transformations. The architecture is designed with enterprise-grade governance, ensuring that AI-generated formulas adhere to organizational data schemas and security protocols. Positioned as a core component of the 'Spotfire Copilot' suite, it prioritizes accuracy and safety through a 'human-in-the-loop' verification interface. As of 2026, the tool has evolved to support multi-step logic derivation, allowing for the creation of sophisticated calculations such as weighted moving averages or multi-layered conditional logic without manual syntax entry. This capability is pivotal for organizations scaling their data-driven decision-making by empowering non-technical analysts to perform advanced statistical modeling within the familiar Spotfire dashboard environment.
Analyzes the existing metadata and column headers to suggest precise column names in formulas without user specification.
Turn every Salesforce data point into an AI-powered prediction and prescriptive action.
Augmented intelligence for business-wide data democratization and governed decision-making.
Enterprise-scale sub-second analytics without the constraints of traditional ETL.
Transform business intelligence with generative AI that answers data questions in seconds.
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Translates English prompts into IronPython or TERR (TIBCO Enterprise Runtime for R) for complex data functions.
Reverse-engineers existing legacy formulas and provides a step-by-step plain English breakdown of the logic.
Uses AI to generate mock data structures based on formula requirements for testing purposes.
Prevents the AI from suggesting scripts that bypass row-level security or utilize restricted functions.
The AI suggests the most appropriate visualization type for the generated calculation result.
Directly links with Spotfire Information Links to understand data relationships before generating join logic.
Analysts struggle to write the statistical logic required to find anomalies in quarterly spending.
Registry Updated:2/7/2026
Creating a churn propensity score usually requires heavy Python coding.
Complex join and math logic for multi-currency global sales reports.