Elixir-Powered System Design

At OneMoreDev, we specialize in designing scalable, fault-tolerant systems that leverage the full power of the BEAM VM and Elixir's concurrency model. With 12+ years of experience across betting, blockchain and AI domains, we build architectures that scale effortlessly and remain resilient under high loads. Our System Design Process section:

Our System Design Process

Discovery & Requirements Analysis

We begin by understanding your business goals, technical constraints, and performance requirements.

Architecture Planning

Our experts design an optimal system architecture leveraging Elixir's strengths in concurrency and fault-tolerance.

Prototype & Validation

We create proof-of-concept implementations to validate critical aspects of the design.

Implementation Roadmap

We deliver a comprehensive implementation plan with clear milestones and technical specifications.

Ongoing Consultation

Our team remains available to guide your development process and provide expertise as needed.

Case Studies

Scaling a Betting Platform to Handle 100,000+ Concurrent Users

Challenge: A betting company needed to handle massive traffic spikes during major sporting events without performance degradation.

Solution: We designed a distributed architecture using Phoenix, leveraging the BEAM's lightweight processes to handle thousands of concurrent connections efficiently. The system included real-time updates via WebSockets and an event-sourced backend for data integrity.

Results: The platform successfully handled 100,000+ concurrent users with sub-100ms response times and 99.99% uptime during peak events.

Real-Time Blockchain Transaction Monitoring

Challenge: A fintech company needed a system to monitor blockchain transactions in real time and detect anomalies instantly across multiple networks.

Solution: We implemented a fault-tolerant Elixir system leveraging GenStage and Flow for parallel stream processing. Each blockchain node was handled as an isolated supervision tree to ensure resilience and recovery.

Results: The platform now processes 50,000+ transactions per second, detects suspicious activity in under 200 ms, and runs continuously with zero downtime during updates.

AI-Based Sports Analytics Platform

Challenge: A sports data startup wanted to deliver live analytics and predictive models during games without delays or service interruptions.

Solution: We built a real-time data ingestion pipeline using Phoenix Channels and PubSub, integrated with a Python-based AI engine. The system streamed updates to dashboards and clients instantly through WebSockets.

Results: Achieved real-time analytics with under 50 ms latency and supported 20,000 concurrent viewers during live matches.