Questions people ask

What does a Forward Deployed Engineer do?

Works inside the customer's real workflow, connects the AI to the tools the team already uses, deploys it, and stays accountable for the result in production. Not a demo handed over.

AI already writes code. What is left to automate?

The typing was automated; the delivery was not. A person still writes every instruction, re-explains the project after every context reset, redoes the rework, and remembers to run the browser test, fix the deploy and write the handover. Those four jobs are where the systems go. See how I work.

Is "70% automated" a real number?

It is counted per run, not estimated. A website-delivery run has ten steps and three of them are human decisions, so seven of ten need no person. The table is on how I work. The count for another workflow is made the same way during the audit and agreed before the build.

What is an MCP connector?

A remote server that ChatGPT, Claude or Codex attaches to, carrying one company's process and rules. The server enforces the steps, so any AI that connects behaves like a trained employee of that company. Explained with an example on what I build.

What happens when the AI's context resets?

Nothing is lost. Every build has an identifier and its state lives in a database rather than in the conversation, so a new session resumes from the last completed step instead of restarting the project.

Can a whole company run this way?

Yes: work enters from the CRM, AI staff do the jobs, one person reviews and approves. It starts as one workflow, not as a company-wide programme. The shape and an example are on what I build.

Will the AI take actions without approval?

High-impact actions keep a human gate, and the exact approval points are agreed before deployment. In a typical run the human steps are the brief, the design approval and the go-live.

Why do you not describe the systems you have built?

Because they are clients' and a company's, not this record's to publish. What each live connector does and refuses is public; the architecture, prompts, agent names and client names are not. The concepts are explained in full with examples instead, and the rest is discussed in the audit.

Can you work with our existing tools?

That is the default. The connectors run from ChatGPT, Claude Code or Codex, and the workflow is designed around the tools, permissions and constraints the team already has.

How is our data protected?

Each part of a system gets only the access its job needs. Data paths, permissions and logs are reviewed as part of the design, and secrets never live in the code.

What happens when a system fails?

Failure paths are designed before launch. The system can retry safely, stop for review, or return the work for correction; the run stays reviewable either way.

Who maintains it afterwards?

The deployment includes documentation and handover. Ongoing optimization reviews failures, improves the evaluations, and extends only what proved useful.

How do we start?

With an audit of one workflow. Bring the job where a person still babysits the AI: see contact.