We describe NABIQ, the system submitted by team Wajeh (واجه) to Tracks A and B of the AISA-ArabicFC shared task on Arabic function calling. NABIQ contains no large language model, no neural network of any kind, no external API and no GPU. It is a CPU-based pipeline of classical ML, retrieval and rule-based repair: a TF-IDF router, a per-tool nearest-argument retriever over train, span extractors with train-mined gazetteers, and a no-call detector. Its core mechanism is an evidence gate: a candidate value may replace a prediction only if it is a normalised substring of the utterance, a member of a closed canonical map, or a date whose tokens occur in the text. On the blind test the frozen system scored Overall A 0.7904 (rank 16) and Overall B 0.8282 (rank 15) at ThinkRate 1.0000. We keep five evaluation levels distinct because the submitted system drops 0.21 ArgEM from development to test. A rehearsal on 545 held-out training rows, run before the blind release, tracked our official Overall A far better than the tuned development estimate (composite error 0.0033 against 0.1724, cancelling larger FnAcc and ArgEM errors); that contrast, not the components, is our result.