Custom data projects
A dataset for the failure you can name.
Suppose your model recommends the same used-car price before and after reconditioning. More rows alone will not explain what changed. Daimon Lab explores permitted histories and expert-created examples around a defined failure.
What could be delivered
Depending on the task and permitted sources, a proposal could specify one or more of these outputs:
- A dataset with documented fields and source information
- Expert task instructions and review criteria
- New demonstrations or corrections for a defined gap
- A separately developed task environment with agreed actions and checks
When the task needs a place to run
A controlled environment can recreate an agreed workflow so an AI system can take defined actions and have the results checked. It can be scoped as custom development.
The first brief
One task and one observed failure are enough to discuss fit. Teams with a specification can also include format, volume, quality, language, timing, and budget requirements.
Agree delivery before production
The schema, sample approval process, acceptance criteria, use rights, and handoff format should be set before work starts. Synthetic content is labeled, and a change in permitted use requires an agreed scope.