Contract-based AI training data acquisition

Operating model

• Every data acquisition engagement is scoped through a written agreement or project authorization.
• The client normally controls the project purpose, dataset requirements, acceptance criteria, and permitted use.
• Bracket Bridge structures recruitment, collection, validation, quality control, de-identification, and delivery according to the agreed scope.

Common dataset categories

• Language, speech, accent, transcription, and evaluation data.
• Human feedback, ranking, preference, annotation, and task-response data.
• Agent trajectory data, including action traces, prompt-response flows, screen or tool-use sequences, and decision-path records.
• Specialized data collection for product testing, model evaluation, or market-specific AI workflows.

Participant handling

Where a project involves human participants, the collection flow should explain what is being collected, why it is collected, whether participation is voluntary, how compensation works, who receives the output, and how withdrawal or deletion requests can be made.

De-identification and anonymization

Project delivery is designed around anonymized or de-identified outputs where the scope allows it. Raw identifiers should be minimized, separated, access-limited, or removed when they are no longer needed for validation, payment, security, or contractual recordkeeping.

Quality and auditability

Datasets are validated against project specifications, collection rules, duplicate or fraud checks, format requirements, market context, and client acceptance criteria. Audit records can be maintained when the client requires them.

Regional compliance posture

US, Hong Kong, EU/EEA, UK, Swiss, and other regional requirements are handled at the project level because the right mechanism depends on participant location, client location, data type, purpose, transfer path, and whether Bracket Bridge acts as processor, service provider, or independent controller.