Project Brief

FiftyFive Technologies partnered with a pioneering shooting sports startup to develop an AI-powered gunshot detection app that enables competitive shooters, range users, and sports enthusiasts to track, log, and analyze their shooting performance in real time. With features like AI-based detection, performance tracking, and accuracy feedback, the platform acts as a digital companion for modern-day marksmen. Built as a Phase-1 MVP, this Flutter-based app leverages advanced AI/ML models, Firebase notifications, and a secure cloud-native backend—laying the groundwork for a scalable, competitive sports tech ecosystem.

Our mission was to create something truly game-changing for the shooting community—and FiftyFive delivered. Their AI expertise, cloud scalability, and design thinking helped us turn a complex vision into an elegant, intuitive product.

Amit Jangid

Founder & CTO

Client & Challenge

The client—a visionary startup in the shooting sports domain—sought to empower shooters with real-time performance analytics via a mobile-first solution. Traditional tracking methods were outdated, relying on manual entry and offering little actionable insight.

Primary challenge: Building a mobile platform capable of accurately identifying gunshots using just a smartphone microphone—even in noisy environments like urban ranges. Key technical goals included:

• > Accurate detection of gunshot audio

• > Real-time performance feedback

• > User privacy & on-device AI inference

• > Scalable backend for future community features

Our Approach

FiftyFive Technologies followed an MVP-first development strategy tailored for sports tech innovators. We combined user behavior insights with a privacy-by-design AI architecture.

AI-first Architecture

• > Integrated YAMNet, Google’s pre-trained sound classification model, as the core of our gunshot detection system

• > Used custom signal fusion logic combining decibel thresholds and IMU sensor motion data to eliminate false positives

On-Device Intelligence

• > Leveraged on-device ML pipelines to deliver low-latency gunshot detection while preserving privacy

Cross-Platform Mobile UX

• > Built a Flutter-based mobile application for iOS and Android with intuitive dashboards and data visualizations

Scalable Backend

• > Developed with Node.js (Domain-Driven Design) and MongoDB, containerized and deployed via Docker on AWS EC2

• > Included Firebase for notifications and Amazon SES for secure communication

Tech Stack

• > Backend: Node.js, MongoDB

• > Mobile App: Flutter

• > AI/ML: Google YAMNet, custom signal fusion algorithms

• > Cloud: AWS EC2 (Dockerized)

• > Notifications: Firebase Cloud Messaging (FCM), Crashlytics

• > Email: Amazon SES

• > Monitoring: Custom dashboard with health metrics

Solution Delivered

We delivered a robust AI-powered gunshot detection app that digitizes and democratizes performance analytics for shooters. The MVP includes:

Gunshot Detection Engine

• > Real-time, on-device engine using YAMNet with motion-sound fusion for gunshot detection

• > Reduces false positives from fireworks, ambient noise, or engine backfires

Smart Shoot Logging

• > Automatically logs date, location, weather, and type of shooting (e.g., clay, upland)

• > Tracks number of shots fired and successful hits (“bag”)

Real-Time Performance Analytics

• > Contextual tagging of shots (motion, decibel, accuracy)

• > In-app trend visualization with suggestions to improve accuracy

Communication Suite

• > Real-time push alerts via Firebase: session summaries, progress milestones

• > Weekly/monthly email reports via Amazon SES with heatmaps and performance data

Scalable Cloud Infrastructure

• > Dockerized AWS EC2 backend supports high concurrency and modular expansion

• > Ready for future modules like video capture, social feeds, and AI coaching

Outcome & Impact

The MVP saw rapid adoption among competitive shooters and recreational users.

Key Results:

• > 90% shot detection accuracy across varied soundscapes

• > 3x increase in session retention due to real-time feedback

• > 70% reduction in false detections

• > Strong user trust due to privacy-preserving, on-device AI inference

By transforming mobile phones into smart shooting coaches, the platform modernized how shooting performance is analyzed and improved.

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