Founder, RACBCONSULTING LLC
I design and build operational systems that connect AI, automation, agents, infrastructure, observability, and governance.
My work focuses on moving AI and automation beyond isolated workflows into systems that can be operated, verified, governed, maintained, and improved over time.
- AI Operations & Automation — operational systems that connect business processes, AI capabilities, workflows, and human decision-making.
- Agentic Systems — governed agents, tools, permissions, execution paths, and validation.
- Voice AI Systems — production-oriented conversational systems for service-business operations.
- Workflow Architecture & Governance — n8n-based automation with lifecycle controls, evidence, recovery paths, and operational ownership.
- Operational Assurance — verification of complete automation implementations, not only individual workflows.
- Self-Hosted AI Infrastructure — maintainable infrastructure for AI and automation workloads.
Private implementation focused on production-oriented voice AI architecture, operational controls, deployment, and lifecycle management.
Private R&D and implementation around governed creation, configuration, and lifecycle management of AI agents.
Private operational orchestration work for AI-assisted execution, task state, evidence, and human oversight.
Private R&D for evaluating complete automation implementations across architecture, controls, observability, security, governance, and operational resilience.
Private implementation focused on structured workflow generation, validation, deployment discipline, and lifecycle management.
Core implementation repositories may remain private. Public, sanitized technical evidence is maintained in the RACB Technical Evidence Center.
I prefer systems that are:
- Observable — operational state and failures can be seen.
- Governable — authority, controls, and ownership are explicit.
- Maintainable — another operator can understand and support the system.
- Secure — capability is constrained by real access controls, not prompt instructions alone.
- Verifiable — implementation claims can be supported by evidence.
- Recoverable — failure paths and human intervention are designed into the system.
- Operationally useful — technology serves a business process rather than becoming the objective itself.
AI & Agents: OpenAI, Codex, Claude, MCP, agentic architectures
Automation: n8n, APIs, webhooks, event-driven workflows
Infrastructure: Docker, Linux, PostgreSQL, Caddy, Cloudflare
Operations: observability, validation, governance, deployment discipline, technical evidence
RACB AI Automation Portfolio / Technical Evidence Center
Public, sanitized evidence from systems designed and implemented by Rene Canete / RACBCONSULTING, including architecture, technical scope, verified technology stacks, selected screenshots, diagrams, validation evidence, and implementation artifacts where appropriate.
RACBCONSULTING LLC works at the intersection of operations, AI automation, and systems architecture, with a focus on practical implementations for service businesses.