SentinelEdge
Privacy-first, on-device scam detection for live phone calls — built in a single day, now accepted to the EMNLP 2026 System Demonstrations Track.
Overview
Built in a single day at the Claude Builder Club Hackathon at ASU, SentinelEdge is a privacy-first, on-device scam detection system for live phone calls. It won 1st place in the AI Tooling track, and the underlying approach has since been accepted to the EMNLP 2026 System Demonstrations Track — I'll be presenting the work in Budapest, Hungary.
Problem
The people most vulnerable to phone scams are often the least equipped with technical defenses — and most existing scam-detection approaches require sending call data to the cloud, a privacy tradeoff that's a non-starter for many of the users who need protection most.
Business Context
Built for a hackathon track judged on real-world AI tooling, differentiated specifically on the privacy-first, on-device constraint rather than raw model accuracy — a deliberate positioning bet that paid off in the judging.
Architecture
Live call audio is analyzed entirely on-device by a compressed, fine-tuned small language model, with periodic federated learning updates improving the model over time — no raw call data ever leaves the device.
Tech Stack
Tradeoffs
The core tradeoff: on-device, privacy-preserving inference over a larger cloud-hosted model — accepting some ceiling on raw accuracy and model size in exchange for zero data leaving the device. Given the target users, that's a deliberate and well-argued tradeoff, not a limitation to apologize for.
Lessons Learned
Privacy constraints aren't just a compliance checkbox — here, they were the actual design driver that made the product usable by the people who needed it most. Designing for the constraint first, rather than retrofitting privacy onto a cloud-first design, shaped every other decision in the build.
Research / EMNLP
The on-device detection approach from SentinelEdge has been developed into a paper accepted to the EMNLP 2026 System Demonstrations Track. I'll be presenting the work at EMNLP 2026 in Budapest, Hungary. Full abstract and demo link will be added closer to the conference.
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