Do. Good. Get it done.
The name is the thesis: an agent isn't useful because it can talk about a task — it's useful when the task is actually, verifiably finished.
Why we built this
Most AI tools stop at a good answer. The gap between "the model said the right thing" and "the work got done correctly" is where teams lose the most time — reviewing, re-running, second-guessing.
dogud started as an internal tool for handling exactly that gap: routine, multi-step operational work that had clear steps but needed constant babysitting to actually finish. We built in verification and approval from day one, not as an afterthought.
Today dogud runs the same way for every customer — plan, act, verify, report — because that loop is what turns an assistant into something you can actually rely on.
What we believe
Verification isn't optional
An agent that can't check its own work isn't done — it's guessing with better vocabulary.
Show the trace
If we can't explain why an agent did something, it shouldn't have done it unsupervised.
Humans stay on the irreversible steps
Speed is only good when it doesn't cost you control over anything that can't be undone.
The team
A small group with backgrounds in distributed systems, applied ML, and operations tooling.
Founding team
Founding team
Founding team
Founding team
Placeholder team entries — swap in real names, roles, and photos once finalized.
We're hiring across engineering and design.
If the problem of "agents that finish things properly" sounds interesting, reach out.