From an overwhelmed startup to an industry challenger
How Enliven Systems built the next generation of athletic analytics — enabling real-time player insights within 40 ms and cutting hardware costs by 68%.
Problem
Lack of real-time health and performance monitoring
High investment and limited flexibility
Fragmented expertise across vendors
Solution
By combining edge computing, Apache Flink, and Kafka, we achieved 40ms end-to-end latency between sensor capture and data visualization. Our self-healing, self-balancing relay network ensured uninterrupted data transmission during live matches and training sessions, even under high levels of interference.
Industry
17 months
Timeline
Location
Engagement model
Project management model
Latency
Achieved real-time results with 40 ms latency, compared to competitors relying on post-event batch processing — transforming how performance data is analyzed and acted upon during live sessions.
Reduced cloud and infrastructure costs by 68% through an edge-first architecture that minimized data transmission overhead and cloud dependency.
Time-to-Market
Accelerated Time-to-Market (TTM) by 4×, delivering a complete, production-ready solution within 17 months via Enliven Systems’ integrated hardware, software, and analytics ecosystem.
Engagement model
Measurable ROI and industry-leading performance
- GDPR
- ISO 27001
Our solutions comply with GDPR and ISO 27001, trusted by clients across regulated industries.
Ultra-low latency data processing
Achieving real-time insights with sub-40 ms latency required a complete architectural overhaul. The system needed to process multi-source sensor data (accelerometers, GPS, heart-rate monitors, and inertial sensors) simultaneously and consistently, even during peak match activity with over 2,000 concurrent telemetry events per player per second.
Traditional streaming frameworks introduced delays of 200–300 ms, which were unacceptable. The team engineered a hybrid event-driven pipeline combining Kafka, Flink, and custom C++ microservices running on edge nodes to ensure deterministic low-latency computation.
Resilient edge–cloud synchronization
Operating in unpredictable stadium environments with unstable wireless connectivity demanded a self-healing data relay layer. To prevent packet loss and jitter, Enliven Systems implemented redundant local buffering, adaptive routing, and predictive network balancing.
The resulting relay protocol automatically rerouted traffic around weak network nodes, maintaining complete data integrity even under 25% signal degradation.
To achieve deterministic sub-40 ms latency and ensure high reliability under stadium-scale load, Enliven Systems engineered a multi-layered architecture combining distributed edge computing, predictive analytics, and scalable orchestration.
Edge and on-prem load balancing with Akka and Kubernetes
The system leverages Akka clusters for distributed actor-based load balancing, enabling seamless data distribution and failover across both edge and on-prem Kubernetes nodes.
This architecture ensures high concurrency, self-healing task orchestration, and resilience under variable network and computational loads.
Each node autonomously manages data routing and recovery, maintaining throughput consistency even during high-density event bursts exceeding 2,000 telemetry messages per player per second.
Real-time predictive analytics with Apache Flink
A dedicated Apache Flink pipeline handles real-time data stream aggregation, transformation, and predictive modeling.
Integrated with Kafka, this pipeline enables immediate anomaly detection and performance forecasting within milliseconds, allowing coaches and analysts to act during live sessions.
Continuous stream snapshots feed into the adaptive analytics layer, updating predictive models on the fly based on player state changes.
Lightweight edge systems for sensor data collection
Custom Debian-based edge systems operate on high-efficiency System-on-Modules (SoMs) optimized for low-power environments.
These units run continuously for up to 3 hours under heavy load, collecting and synchronizing over 45 concurrent sensor streams, including motion, heart rate, and biometric data, directly from the human body.
Local preprocessing minimizes data volume before transmission, conserving bandwidth and reducing dependency on centralized infrastructure.
Data persistence and synchronization
Processed data is stored in a MongoDB cluster optimized for high-frequency write operations and low-latency queries.
The hybrid edge–cloud synchronization model ensures data consistency between on-premise and remote nodes, providing both real-time visualization and long-term analytical storage.
MongoDB’s flexible schema enables seamless integration with downstream analytics, AI modules, and third-party dashboards.
Some of the more than 20 technologies we use.
C++
MongoDB
Flux CD
Scala
Akka
Angular
The Enliven Systems advantage
Enliven Systems helps ambitious companies turn data into a competitive advantage through cutting-edge AI engineering, research, and cloud optimization.
Distinguished talent pool
Predictable delivery
Experienced researchers
Success in a broad spectrum of applications
Take the first step toward real-time performance intelligence that transforms how data empowers athletes, coaches, and organizations:
- Deliver live insights in under 40 ms, outpacing competitors who rely on delayed post-match analysis
- Cut infrastructure costs by up to 70% with our edge-first, hybrid architecture
- Scale predictive analytics 4× faster using real-time stream processing with Apache Flink
- Stay compliant and secure, every solution meets GDPR and ISO 27001 standards by design
Book a meeting today to unlock your competitive edge and bring next-generation athletic intelligence to life, faster, leaner, and smarter than ever before.
