Deployment
Why pods beat traditional consulting
In short: FDE pods embed certified builders and eval engineers who ship in production and transfer capability: unlike traditional consulting models that often exit at go-live.
Published 2026-04-28 · Updated 2026-07-01
The consulting handoff problem
Traditional AI consulting excels at strategy decks and proof-of-concepts. At go-live, the team changes, documentation is thin, and client engineers inherit a system they didn't build. Evals are an appendix. Knowledge walks out when the SOW ends.
What pods optimize for
- Shared methods: FDE and FDEE trained together on the same stack
- Production commits: code in client repos, not vendor sandboxes
- Parallel capability transfer: pairing from week one
- Eval coverage: FDEE on harnesses before and after go-live
When to choose each model
Consulting fits early strategy and vendor selection. Pods fit when you need governed production in weeks, especially in regulated environments where eval discipline and embed accountability matter.