Case Study | AI-Powered Gunshot Detection & Real-Time Sync Platform for Shooting Sports
AI Gunshot Detection & Real-Time Sync for Shooting Sports
Sports TechnologyAI/MLReal-Time SystemsOffline-First Mobile
FiftyFive Technologies built an AI-powered mobile MVP that detects gunshots, syncs sessions in real time, and maintains accuracy in low-connectivity areas. Acoustic ML and recoil validation improve detection, while offline-first architecture prevents data loss.
The client is a UK-based sports technology company operating in the outdoor recreation space. The company set out to close a clear gap: shooting sports had no digital tooling to measure, analyse, and improve performance the way other sports do. Their product vision was a mobile-first platform that automatically registers gunshots, synchronises performance data across multiple participants in a session, and stays reliable in rural environments where connectivity is weak or absent. FiftyFive Technologies was engaged as the engineering partner and provided a dedicated cross-functional team to take the MVP from architecture through to deployment.
Challenges
The Challenges That Started It All
Detecting a single gunshot on a live range is an acoustic problem before it is a software problem — dozens of near-identical sounds arrive within milliseconds of each other. Delivering that on consumer phones, in rural areas, across shared sessions, added a second layer of engineering constraints on top.
Acoustic source separation. The platform had to distinguish a user's own gunshot from background noise and from shots fired by other participants nearby. Without reliable separation, every shooter on a range would register every other shooter's rounds, making performance data meaningless from the first session onward.
Real-time multi-user state synchronisation. Multiple participants share a live session, and each one's shot events must appear on every other device almost immediately. Doing this over intermittent rural connections — while keeping latency low enough to feel instant — meant designing for network conditions that fluctuate constantly rather than assuming a stable link.
Continuous on-device processing within battery limits. Audio and accelerometer streams had to be processed continuously throughout a session for real-time inference. Running always-on capture and classification on a phone without draining the battery required careful optimisation of the background processing pipeline on both iOS and Android.
Offline-first operation with zero data loss. Shooting ranges sit in exactly the locations where mobile coverage fails. Sessions had to run fully offline, capture and store every event locally, and reconcile with the server once connectivity returned — with no dropped shots, no duplicates, and no manual recovery from the user.
Hardware normalisation across devices. Microphone sensitivity, accelerometer calibration, and audio pipelines vary widely across iOS and Android hardware. Raw signals had to be normalised so that detection accuracy stayed consistent regardless of which phone a participant brought to the range.
Solution
Solutions We Delivered
FiftyFive Technologies designed the platform around a single principle: the phone should be able to do the hard work alone, and the network should only ever be an enhancement. Shot detection runs entirely on-device through machine learning inference, so accuracy does not depend on connectivity. State is written locally first and synchronised opportunistically. Real-time collaboration sits on top as a room-based layer, and the backend is structured so that every captured session feeds a data pipeline for future model retraining. The result is an MVP that behaves identically on a connected range and a dead-zone one.
Hybrid Shot Registration Engine
The core of the platform is a multi-signal detection engine rather than a single classifier, because audio alone cannot reliably separate a user's own shot from a neighbour's.
TensorFlow Lite–based acoustic AI performs the primary gunshot classification.
Accelerometer-driven recoil validation confirms that the detected shot was fired by the device's own user.
Decibel-sensitive spike detection differentiates near and distant gunshots as an additional validation layer.
The three signals are combined so that a registration requires corroboration, not a single trigger.
On-device ML inference across iOS and Android
Classification models run locally on both platforms, enabling frequency-based gunshot detection in real time.
TensorFlow Lite audio classification models deployed to iOS and Android.
Real-time inference performed on-device, with no round trip to a server required for detection.
Offline-first architecture ensures the engine operates fully in low-connectivity environments.
Detection continues uninterrupted during network disruptions, preventing data loss at the source.
Offline-first state management and background sync
The application treats the local device as the source of truth during a session and reconciles afterwards.
Redux Toolkit manages application state across the session lifecycle.
SQLite provides durable local persistence for shot events and session data.
Background sync reconciles local state with the backend once connectivity is restored.
Sessions started offline complete normally and upload without user intervention.
Real-time backend and room-based synchronisation
A Node.js backend coordinates live sessions between multiple participants.
Express.js API layer handles core application and session endpoints.
Socket.IO delivers room-based real-time synchronisation so participants in a session see events as they happen.
BullMQ with Redis runs asynchronous job queues for notifications, emails, and data processing, keeping heavy work off the request path.
Firebase Cloud Messaging and APNs deliver push notifications reliably to both active and offline users.
Data layer built for integrity and future retraining
The persistence design serves the MVP today and the ML roadmap next.
PostgreSQL schema structured for relational integrity across users, sessions, and shot events.
AWS S3 pipeline captures audio and session artefacts for future ML model retraining.
The data foundation allows detection accuracy to improve as real-world session volume grows.
Tech Stack
Tools That Powered the Build
AI / ML
TensorFlow Lite
Mobile
iOSAndroidRedux ToolkitSQLite
Device Sensors
AccelerometerMicrophone / Decibel Capture
Backend
Node.jsExpress.js
Real-Time
Socket.IO
Queues & Caching
BullMQRedis
Database
PostgreSQL
Cloud & Storage
AWS S3
Notifications
Firebase Cloud MessagingAPNs
6-Person Team
Team structure
6
Cross-Functional Delivery Team
Results
Results That Speak Clearly
Project DurationApr 2025 – Jun 2026
The MVP shipped as a production-ready platform that registers shots accurately, synchronises participants in near real time, and loses nothing when the network does. Beyond the technical benchmarks, the client gained a scalable engineering foundation and a faster route to market.
>95% Detection Accuracy
FiftyFive's hybrid Shot Registration Engine achieved over 95% gunshot detection accuracy with under 0.5% false positives across live sessions.
Sub-200ms Sync Latency
The Socket.IO room-based architecture delivered sub-200ms synchronisation latency between participants, keeping multi-user shooting sessions effectively instantaneous.
Zero Data Loss Offline
Offline-first architecture with SQLite persistence and background sync produced zero data loss under connectivity drops in rural range environments.
<5% Battery Impact
Optimised background audio and sensor processing added under 5% additional battery drain per active hour of continuous session use.
AI gunshot detection uses acoustic machine learning models that analyse audio frequency patterns to identify the signature of a gunshot. On mobile, these models run on-device using frameworks like TensorFlow Lite, enabling real-time inference without a server connection and keeping detection accurate even in offline environments.
On-device machine learning runs inference directly on a phone rather than sending data to a cloud server. It matters because it removes network latency, works fully offline, reduces bandwidth costs, and keeps sensitive audio or sensor data on the user's device. TensorFlow Lite is the framework used here across iOS and Android.
Offline-first mobile apps store data locally first and synchronise later. This typically combines a local database such as SQLite with a state management layer like Redux Toolkit, plus a background sync process that reconciles with the backend when connectivity returns. The result is zero data loss during network drops.
Real-time multi-user apps commonly use Node.js with Express.js for the API layer, Socket.IO for room-based websocket synchronisation, Redis with BullMQ for asynchronous job queues, and PostgreSQL for relational data integrity. This stack supports low-latency event delivery across many simultaneous participants.
Continuous audio processing drains battery through always-on capture and inference. Optimisation involves lightweight quantised ML models, efficient background processing scheduling, sensor-triggered activation rather than constant full-pipeline execution, and platform background modes. Well-optimised implementations can hold additional drain under 5% per active hour.
MVP development builds the smallest production-ready version of a product that delivers real value and validates the market. Timelines depend on technical complexity — a straightforward mobile MVP may take three to six months, while AI-powered platforms requiring custom model development and real-time infrastructure typically run longer.
Yes, but audio classification alone is often insufficient when near-identical sounds overlap. Reliable systems combine multiple signals — for example pairing acoustic classification with accelerometer or decibel-level validation — so that a detection requires corroboration from independent sensors rather than a single audio trigger.
Sports technology software development builds digital platforms that measure, analyse, and improve athletic performance. It typically spans mobile apps, wearable and sensor integration, real-time data synchronisation, performance analytics, and community features — increasingly powered by AI for automated event detection and personalised insight.
Consistency requires normalising hardware variation. Microphone sensitivity, sensor calibration, and audio pipelines differ across devices, so raw signals are calibrated to a common baseline before processing. Combined with device-tier testing, this keeps accuracy stable regardless of the handset used.
Yes. Dedicated cross-functional teams typically combine AI/ML engineers, mobile developers, backend engineers, and QA under a single delivery lead. FiftyFive Technologies provides time-zone-aligned dedicated teams with flexible engagement models and monthly billing on actual man-hours, scaled to project scope.
Retraining pipelines capture real-world inputs and outcomes during live use and store them in cloud object storage such as AWS S3, alongside structured metadata in a relational database. This creates a labelled dataset that grows with usage, allowing detection models to be retrained and improved over time.
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