Audio, language, and context

Data for AI that can listen, see, and understand context.

Collection and annotation programs spanning speech, audio, text, image, and video—designed as connected signals rather than isolated files.

Signal map

Program outcomes

Natural interaction data

Context-rich annotations

Multilingual coverage plans

Inside the work

Human expertise, connected to a controlled data workflow.

Every engagement links the people doing the work, the evidence used to review it, and the model behavior the program is meant to improve.

Capabilities

Review layer included

Prompted, conversational, and in-domain audio gathered to defined demographic and acoustic specifications.

Review layer included

Timestamped speech, speaker turns, paralinguistic features, and language metadata.

Review layer included

Cross-modal labels for visual question answering, grounding, captioning, and instruction following.

Review layer included

Human assessment of quality, relevance, naturalness, and task success.

What gets delivered

The exact artifacts change by program, but every delivery is designed to be inspectable, actionable, and ready for the next model decision.

01 / Design

Population, environment, consent, and signal requirements in one operating brief.

02 / Data

Timestamped speech, speaker metadata, and linked visual or textual context.

03 / Insight

Coverage, signal quality, and exception findings prepared for program decisions.

Delivery model

Programs move in visible stages, with a review point before scope, volume, or complexity increases.