For companies building AI

An AI development team needs to know what its examples represent and why they belong in the work. ADCO can scope authorized collection, preparation and annotation around your taxonomy, intended use and output format. Permissions, provenance and the treatment of uncertain or unsuitable material belong in the initial definition.

Text, images, audio and video each raise different instruction and qualification needs. Confirm the required language, subject knowledge, tools and processing arrangements before deciding how the work should be organized.

Human assessment can help a team examine response relevance, factual support, instruction following, preference and defined agent behavior. The evaluation needs a rubric, a suitable reference basis and a way to record disagreement. An aggregate score should preserve the context of what was tested and what remains uncertain.

ADCO's scope can include preference feedback and fine-tuning data preparation. Model engineering and the operation of training infrastructure are separate responsibilities that must be established independently. Authorized task testing and scoped safety review require agreed environments and boundaries.

The useful output is a record your team can act on: structured annotations, reasoned judgments, recurring issue categories or evidence attached to an exception. Discuss how findings will be received, how material questions reach the right owner and which changes require renewed calibration.

Choose managed delivery for agreed outputs and coordination, or staff augmentation when your team will direct the workflow. Data access, review coverage, acceptance and release responsibilities are defined for the engagement. Human evaluation contributes evidence to a development decision; it does not certify a model as safe for every use.