Forward Deployed AI Engineer

Forward Deployed AI Engineering glossary

The working vocabulary of Forward Deployed AI Engineering for founder-led B2B SaaS. Plain definitions of the role, the offer, the deliverables, and the tools, written the way they are actually used.

By Nasser Ghanemzadeh. Last updated June 2026.

Forward Deployed AI Engineer
An engineer who embeds inside a customer's team and ships AI capabilities directly into the customer's product and codebase, rather than delivering a deck, a dashboard, or a demo. The delivery model comes from Palantir. OpenAI and Anthropic both launched deployment subsidiaries using it in May 2026. See the full explainer: what is a Forward Deployed AI Engineer?
AI Feature Sprint
A ten-business-day engagement that ships one narrow AI workflow into a customer's product. Fixed scope, fixed price, end-to-end delivery and handoff. See the AI Feature Sprint.
Discovery Day
A two-hour paid working session, priced at $500, to assess whether an AI use case is useful, buildable, and worth a Sprint. The output is a go/no-go memo within 24 hours, credited toward a Sprint if the customer continues. See Discovery Day.
Workflow
A specific process a user runs, with a clear input and a clear output, that produces a measurable improvement. It is the unit of work shipped in a Sprint. Not a feature, and not a platform.
Output Quality Rubric
A practical checklist, usually on a 1-to-5 scale, that defines what good output from an AI workflow looks like, how the customer's team reviews it, and how the workflow improves over time. It ships as a deliverable with every Sprint.
Sprint Brief
The one-to-two-page document written on Day 1 of a Sprint and signed off by the customer's decision-maker, defining scope, success criteria, deliverables, and what is explicitly out of scope. It is part of the contract.
Workflow Playbook
A short handoff document covering a workflow's purpose, inputs, expected outputs, review steps, edge cases, and maintenance guidance, so the customer's team can run and improve it without the engineer.
Model Context Protocol (MCP)
The open standard Anthropic introduced for letting AI models talk to external systems and data. Many AI workflows connect to a customer's tools and data through MCP servers.
Claude Code
Anthropic's command-line tool for agentic coding. A primary delivery tool for shipping AI workflows directly into a codebase.
AI-native product
A product built around AI workflows from the start, rather than a traditional product with an AI feature added on later.
Vectig
Nasser Ghanemzadeh's AI-native startup finance product: cash-flow forecasting, runway scenarios, and investor update drafting. It serves as the live R&D lab where workflow patterns are proven before they ship to customers. See vectig.com.

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