Embedded AI Engineer On Site, the Forward Deployed Engineer Model

The model starts with goals defined together. We map the core processes, set quarterly targets, and agree that success is measured by processes actually running rather than by the number of workshops held. Everything the sprints do flows from that baseline.

Then the engineer comes on site, on fixed days, one or two full days a week depending on the scope of the goals. Consistency is the whole point: people know when the engineer arrives, they bring real processes from their own desks, and work advances every week instead of being deferred to the next big project. The engineer learns the process from the people who run it, not from a specification document.

Work runs in two-week sprints. Each sprint opens with defined goals and closes with a working output: an agent in production, an automation that removes manual work, or a system that reached actual users. Visibility is full throughout. Once a month there is a retro session that reviews what was built, what is genuinely being used, and sharpens priorities going forward, including metrics for leadership.

What gets built falls into three categories. Agents on your existing AI systems: enterprise versions of ChatGPT, Claude, Copilot or Gemini, agents for each department's specific processes, and a shared Skills library that grows week over week. Automations connected to your systems using n8n, Zapier or Power Automate, whichever is already installed, including connections to internal systems through MCP. And Vibe Coding systems: internal tools, dashboards and small applications built with AI, from idea to working version within a sprint or two, for the cases where standard tools fall short.

What we need from your side is minimal but specific: an executive sponsor who approves goals and clears obstacles, a central point of contact who coordinates with the engineer, and the practitioners themselves who bring the processes. Implementation is not an event, it is a muscle. When the model runs properly, the organization ends up not just with working agents but with people who know how to build and maintain the next ones.