Dysnix

Dysnix is a company that provides DevOps, MLOps, cloud infrastructure and blockchain infrastructure services. The organisation operates from Estonia and serves clients across multiple regions as implied by its international client list in blockchain, AI, financial technology and healthcare sectors.

Services and Capabilities

• DevOps and Infrastructure Delivery: design and deployment of cloud and bare-metal environments using infrastructure-as-code (IaC), automated deployment pipelines, and observable production stacks.
• MLOps and Model Production: end-to-end model training, deployment, monitoring and automation for production machine learning workflows, including real-time performance tracking.
• Blockchain Operations: operation of high-performance RPC clusters, self-hosted validator infrastructure, and real-time blockchain data extraction with ETL processing.
• Cloud FinOps and Cost Optimisation: cloud cost audits, forecasting and continuous optimisation to reduce infrastructure spending and improve resource efficiency.
• Availability and Scaling Engineering: implementation of auto-scaling, predictive scaling engines and failover mechanisms to target high availability and rapid recovery.
• Security and Compliance Engineering: multilayered security measures, threat mitigation and alignment with common compliance benchmarks for production systems.

Industry Focus

• Blockchain and Web3: running RPC nodes, validators and data pipelines for decentralized applications and exchanges.
• Financial services and FinTech: infrastructure design, high-availability deployments and security measures for transaction-focused platforms.
• Healthcare / MedTech: secure infrastructure and proactive threat mitigation for medical technology deployments.
• AI and Analytics: productionising machine learning models, autoscaling of AI workloads and implementing monitoring/observability for model performance.
• Enterprise SaaS and Platforms: end-to-end infrastructure delivery, cost optimisation and operational support for high-throughput services.

Technology and Delivery Approach

• Work is executed through infrastructure-as-code, automated CI/CD pipelines and observability tooling to enable repeatable deployments and monitoring in production.
• Data engineering patterns include ETL pipelines for blockchain data and real-time ingestion for downstream analytics.
• Automation and scaling techniques include horizontal/vertical autoscaling, predictive scaling driven by ML models, and resource orchestration to match load patterns.
• Cost management is addressed through audits, forecasting and continuous optimisation of cloud resources and workloads.
• Security practices include layered protections, proactive threat mitigation and alignment with common compliance frameworks for regulated deployments.
• Delivery model includes provision of dedicated engineers or teams responsible for end-to-end infrastructure design, implementation and operational handover.