Pecan AI

Pecan automatically analyzes raw data columns and generates derived features — such as aggregations, ratios, time-based trends, and interaction terms — that improve model predictive power. This eliminates the most labor-intensive phase of traditional ML projects, where data scientists manually explore and construct features. The platform tests thousands of potential features and selects only those that meaningfully contribute to prediction accuracy.

The platform offers ready-to-deploy solution templates for high-demand use cases including customer churn, lifetime value, demand forecasting, and fraud and chargeback prevention. Each template comes pre-configured with appropriate model architectures, target variable definitions, and evaluation metrics, allowing teams to go from data connection to predictions in days. Templates can be customized to fit specific business definitions and data schemas.

Pecan's point-and-click interface guides users through the entire model building workflow — from selecting a data source and defining a prediction target to reviewing model performance and deploying to production. Business analysts can configure and iterate on models without writing any code, while the platform handles algorithm selection, cross-validation, and hyperparameter tuning behind the scenes.

Once models are deployed, Pecan continuously tracks their prediction accuracy against real-world outcomes to detect performance degradation. The platform monitors for data drift, concept drift, and distribution shifts that could compromise prediction quality. When accuracy drops below defined thresholds, automated alerts notify the team and guide them through the retraining process to restore model performance.

Pecan provides clear explanations of which data features most influence each prediction, helping business users understand why the model produces specific scores or classifications. This transparency enables stakeholders to validate that model logic aligns with business intuition and regulatory requirements. Explainability reports can be shared across teams to build organizational trust in AI-driven decision-making.