Most AI tooling projects stall because the outputs don’t plug into how the team actually works. I build agent workflows that integrate with your existing tools — Linear, Notion, Slack, your CRM — and automate the repetitive parts of the ops stack that eat time without creating value.
What this looks like in practice
A typical AI orchestration engagement focuses on one high-leverage workflow — lead research, content production, or internal ops tooling — and builds an end-to-end agent that handles it reliably. Claude Code and MCP servers connect the AI layer to your actual data and tools; GPT-4 or Anthropic models handle the reasoning; and the whole thing runs on your infrastructure, not mine.