We present SENTINELEDGE, an open-source streaming detector for English-language scam phone calls that scores speech sentence-by- sentence and raises alerts while a conversa- tion is in progress, protecting callers rather than explaining losses afterward. It targets multi-minute scripted fraud calls, where sys- tems are closed and un-auditable and aca- demic alternatives use neural backbones un- suited to on-device budgets. SENTINELEDGE uses a streaming framework with per-sentence scoring, exponential moving average smooth- ing, and threshold-triggered alerts. It com- pares classifier heads using hand-crafted fea- tures, TF–IDF, SVM, gradient-boosted trees, and fine-tuned transformer on identical inputs and robustness conditions, revealing storage– accuracy trade-offs. We report bootstrap con- fidence intervals, stress-test ASR-style noise, adversarial scam phrasings and retraining, and zero-shot transfer to SMS and phishing URLs. Code, models, evaluation harness, and demon- stration are released under the MIT license