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Thunk.AI vs FDE-led deployments
Many AI vendors deliver automation by sending forward-deployed engineers to build each workflow for you. That is slow and expensive, but can work until you want the second workflow, or need the first one changed. Thunk.AI gives your process owners a Forward Deployed Agent and a reliable no-code platform instead.
Side by side
Six differences that decide how far you get
FDE-led deployment
Thunk.AI
Who builds
The vendor’s engineers, in code
Your process owners with the Forward Deployed Agent, as no-code thunks
Who operates
The same engineers keep it running and make every change
Thunks run autonomously; the agent troubleshoots and improves them on request
Reliability
Depends on each team’s harness engineering, so people check the AI’s work
Built into the platform, so people handle only the exceptions
Speed
Each new workflow waits for engineering capacity
The agent does weeks of specialist work in minutes
Cost
Services fees that grow with every workflow
A flat annual platform fee for a private instance, with support and training included
Ownership
Knowledge leaves with the engineers
Your teams own thunks they can read and change themselves
Why it matters
The difference compounds with every workflow
Cost
With an FDE-led model, the bill grows with every new workflow and every change to an old one. Thunk.AI is a flat annual platform fee for a private instance, with support and training included, so adding workflows doesn’t add services fees.
Ownership
When the engineers move on, the knowledge of how each workflow works goes with them. A thunk is readable by the people who run the process, so what you build stays yours, and the next change doesn’t start with a statement of work.