Forward deployed engineering

Discovery means watching the work, following exceptions and agreeing what a successful result looks like. Engineering then connects the workflow to the right data, permissions and user interface. The role needs both activities: a good demonstration alone does not establish a working service.

For an illustrative tender team, that could mean finding approved evidence, drafting an answer with references and routing it to a reviewer. The engineer would also design what happens when evidence is missing. Our forward deployed engineers service applies that model to B2B AI work.

Start with a sample of real work and write down acceptance criteria before building. Separate a useful draft from an approved action. Test missing data, conflicting instructions and permission failures as well as the straightforward examples. An embedded engineer may deliver an AI knowledge base or a bounded assistant; LLM evaluation provides the task evidence. Claude Code training can develop the receiving engineering team’s assisted repository workflow alongside the actual application handover.

A pilot becomes production work when somebody owns monitoring, support and the fallback process. AI deployment services cover that transition. AI training and consultancy connects the build to the people who will use and maintain it.

Agree the baseline with the team doing the job: how long the work takes, how often it needs correction and which exceptions matter. Use comparable tasks when checking the new workflow. A claimed time saving is incomplete if reviewing output takes that time back.

Keep source code, access arrangements, evaluation examples and operating notes in systems your company controls. An AI evaluation consultant can help define acceptance evidence; our AI implementation consulting guide explains how those decisions fit together.