MLOps · AI Security · EU AI Act
Build ML Platforms. Secure Your AI.
We build production ML platforms, defend against prompt injection and model theft, and ensure EU AI Act compliance. So you can secure.
100%
Audit Pass Rate
50+
AI Systems Protected
24/7
Monitoring
Technology stack
KubeflowMLflown8nSupabaseVercelPostgreSQLAWS
Our Services
MLOps. AI Security. Compliance.
Production ML infrastructure
Build and operate production ML pipelines with experiment tracking, feature stores, model registries, and automated retraining. From prototype to production-grade ML infrastructure.
- ML pipeline orchestration (Kubeflow, Airflow)
- Experiment tracking & model registry (MLflow)
- Feature store design & implementation
- Model monitoring & drift detection
- ML CI/CD & automated retraining
Protect your AI systems
Defend against prompt injection, data poisoning, and model theft. We ensure EU AI Act compliance and provide 24/7 threat monitoring.
- Prompt injection defense & input validation
- AI security audits & threat modeling
- LLM red teaming & penetration testing
- 24/7 monitoring & incident response
Regulatory compliance
Navigate the EU AI Act with confidence. Risk classification, conformity assessments, documentation, and audit preparation for AI systems in the European market.
- AI system risk classification
- Conformity assessment preparation
- Technical documentation & audit trails
- Human oversight implementation
n8n • MuleSoft • Talend
Expert-level integration workflows with production-grade error handling, monitoring, and governance across all major platforms.
- Event-driven pipelines & API orchestration
- AI model integration (OpenAI, Claude, Llama)
- Secure credentials & observability
- Cross-platform expertise
Knowledge Hub
Latest Insights
Expert guides on MLOps, AI security, EU AI Act compliance, and enterprise automation.
MLOps for Small Teams: Building ML Infrastructure Without the Complexity
Build effective MLOps infrastructure with a small team. Covers the minimal viable MLOps stack, tool selection, automation priorities, and scaling strategies for teams with 1-5 ML engineers.
From Jupyter Notebook to Production: A Practical MLOps Migration Guide
Step-by-step guide to migrating ML code from Jupyter notebooks to production-ready pipelines. Covers refactoring patterns, testing, CI/CD integration, and deployment with real before/after examples.
ML Experiment Tracking: Best Practices for Reproducible Machine Learning
Master ML experiment tracking with practical patterns for reproducibility. Covers what to log, how to organize experiments, comparison workflows, and team collaboration with MLflow and Weights & Biases.
Common MLOps Mistakes and How to Avoid Them
Learn from the most common MLOps failures. Covers training-serving skew, missing monitoring, manual deployments, and 12 other mistakes that derail production ML systems.
MLOps Tools Comparison 2026: The Complete Stack Guide
Compare the top MLOps tools across every category in 2026. Covers experiment tracking, pipeline orchestration, feature stores, model serving, monitoring, and data versioning with recommendations.
GPU Infrastructure for ML: Cost Optimization and Scaling Strategies
Optimize GPU infrastructure costs for ML training and inference. Covers spot instances, multi-GPU training, right-sizing, scheduling strategies, and cost monitoring for cloud ML workloads.
FAQ
Frequently Asked Questions
Quick answers about AI Security, n8n automation, and our services. If you don't see your question, reach out via the contact form.
