Company
Flux operates a decentralized cloud platform that connects distributed GPU resources for AI and machine learning workloads. The company competes in the AI infrastructure market where timing determines market leadership and delays can mean losing ground to better-funded competitors.
The Challenge
Flux faced a time crisis that threatened its ability to compete. In AI—arguably the fastest-moving sector in history—new breakthroughs constantly reset the playing field, shifting both the opportunities and the competition. To keep pace, the company needed to launch its Kubernetes-based GPU cluster platform quickly. But the technical complexity created a catch-22: decentralized cloud infrastructure demands deep expertise in container technology, microservices architecture, and large-scale system administration—skills that are scarce, expensive, and highly competitive to recruit. As a self-funded startup, Flux couldn’t outbid larger players on salary, and it couldn’t afford to lose precious weeks to traditional hiring cycles. Filtering through hundreds of LinkedIn applications, scheduling interviews, and assessing technical fit drained the very resource Flux needed most: time.
The Solution
Flux turned to A.Team's AI-powered platform to find talent with the precise, uncommon skill set they needed: deep Kubernetes expertise combined with experience in AI workloads, cluster building, decentralized computing, and building category-defining products. Within days, A.Team's platform analyzed work experience, past project data, and working relationships across their 11,000+ builder network to match Flux with A.Team Builder Nick Kammerdiener, who had the exact combination of technical skills and market understanding they required.
Kammerdiener brought experience optimizing GPU clusters for machine learning tasks and understanding the pitfalls of Kubernetes implementations. He hit the ground running, implementing collaborative systems where the team could evaluate ideas and trade-offs together. His prior experience in the field allowed him to anticipate technical challenges and guide the team around common Kubernetes obstacles, accelerating development. Kammerdiener also trained Flux's team on how to resolve technical issues when he wasn't available, building operational independence that extended beyond his direct involvement.
Technologies Used
Kubernetes: Orchestration system managing distributed GPU nodes across Flux's decentralized network
GPU Cluster Infrastructure: Parallel processing architecture for AI and machine learning workloads
Decentralized Computing Systems: Distributed network architecture for cloud compute services
"If you ever post a job on LinkedIn, the amount of responses you get is ludicrous. We had people spending weeks trying to filter through them. But the AI that A.Team created has really sped up things from weeks to minutes for our recruitment."
Davy Wittock, Chief Business Officer, Flux
The Results
Flux released their decentralized AI platform ahead of schedule, allowing them to incorporate B2B models originally planned for year-end and position themselves ahead of competitors in the AI infrastructure market.
Accelerated Time-to-Market
Launched 90 days ahead of projected deadline, with 25% faster overall execution.
GPU Network Scale
Tens of thousands of GPUs registered on the network, providing the capacity and redundancy required to support enterprise-level AI workloads.
Security & Usability
The A.Team builder implemented safety and security controls for user data while creating user friendly software.
Market Recognition
Major AI players already engaging with Flux based on alpha-stage software, with users praising ease of deployment.
