A ccedil;ai (Euterpe spp.) is a flagship product of the Amazon bioeconomy, recognized for its high nutritional value and economic importance. The commercialization of a ccedil;ai could benefit from a straightforward certification or traceability system that verifies geographic origin and product authenticity. This study introduces the first isotopic assignment model for a ccedil;ai berries from the Brazilian Amazon, utilizing stable isotopes of oxygen (delta 18O) and hydrogen (delta D). We developed Random Forest-based isoscapes and employed Bayesian assignment techniques to create spatial posterior probability surfaces for 59 samples with known origins. The model's evaluation included spatial performance metrics at sample level: posterior quantile rank, distance to the highest posterior cell, and the size of the 95 the median 95.0 indicating a strong capacity for exclusion. Sixty-eight percent of samples had quantile ranks below 0.10, and 655.29 (95 0.25-0.34), classifying the model as ``good''. We conclude that this model is best suited for exclusion-based applications like isotopic provenance certification rather than for exploratory geographic assignments lacking prior location information. This research demonstrates the practical feasibility of using stable isotopes for traceability in tropical forest products and introduces transferable spatial metrics for future isotopic provenance modeling.