What is a forward-deployed engineer? When to hire one
What a forward-deployed engineer does, what the role owns, and when it's worth bringing one in to ship an AI workflow.
These guides cover how the roles work, what a build costs, and what to check before you sign. Use them to pick one workflow and ask sharper questions before you commit a budget.
What a forward-deployed engineer does, what the role owns, and when it's worth bringing one in to ship an AI workflow.
To hire a forward-deployed AI engineer, write down the first workflow, test for production judgment, and agree who owns the code before work starts.
How AI consulting and forward-deployed engineering differ: who decides, who delivers, what you get at the end, and what a handoff between teams costs.
Both roles sit close to the customer. They differ on product boundary, commercial role, delivery scope, and ownership after launch.
When to hire an in-house AI automation engineer and when to use an agency, judged on workload, speed, context, cost, and ownership.
What AI implementation covers after the demo works: the workflow, software, data, integrations, testing, controls, launch, and ownership.
What moves the cost of AI implementation, and how we price a $5,000 sprint, a $20,000+ MVP, or a larger scale-up.
A checklist for choosing an AI implementation partner. Check the workflow plan, the team, the controls, code ownership, and the exit path.
To take an AI pilot to production, pick one workflow, connect the real systems, test it, add controls, launch small, and name an owner.
Pick the simplest design that works. Choose an AI agent or workflow automation by how much the work varies, the judgment it needs, the risk, and the cost.