Response evaluation
Preference, safety, accuracy and helpfulness review.
Human Loop helps AI teams collect rights-cleared evaluations, recordings, annotations, conversations and agent tests from qualified contributors, with quality controls built in.

Voice, video, image, agent testing and response evaluation running through QA.
New models need specific human signals: preference judgments, regional speech, visual labels, task attempts, consented video, and real-world agent feedback.
That is an operations problem. You need sourcing, qualification, privacy, fraud checks, payments, review queues and clean dataset delivery in one loop.
Preference, safety, accuracy and helpfulness review.
Language, accent and scenario-specific speech data.
Consent-based multimodal collection and review.
Labeling, classification and visual QA outputs.
Humans test AI agents in real workflows.
Guided dialogues, rankings and conversation data.
Define modality, task rules, contributor profile, budget and delivery format.
Route work to qualified contributors by language, skill, location and quality.
Run guided tasks for voice, video, text, image, agent tests and reviews.
Use qualifications, hidden gold tasks, fraud signals and human review.
Approve records and download structured CSV, JSONL and audit metadata.
Every task moves through qualification, reservation, submission, automated scoring, review and ledger records before it becomes exportable dataset material.
Create the campaign, run the contributor loop, approve high-quality records, and export the dataset.
Launch Human Loop