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Clap Data Missions · Design-partner stage
Human behavior data, created for the question your model cannot answer.
Clap turns precise data needs into playable mobile missions that produce fresh video, voice, choices, timing, transcripts, structured labels, and quality metadata.
78%challenge completion across the Clap platform
Livevideo + voice capture in production today
Nowinviting design partners
How a Data Mission works
Illustrative workflow
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Clap turns it into a mission
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People choose, speak, react, and perform
videovoicechoicetiming
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Responses become structured records
Prompt matchPass
A/V quality4.4
Label confidence.91
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Deliver against the partner spec
JSONLCSVMedia
Illustrative workflow
1
Define the missing signal
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Turn it into a playable mission
videovoicechoicetiming
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Deliver structured records
JSONLCSVMedia
Why Clap Data Missions
Purpose-created
Collect the behavior a model needs instead of settling for whatever already exists online.
Multimodal by default
Combine video, voice, choices, timing, and explanation in one response.
Playable collection
Challenge mechanics and incentives help people start, finish, and respond naturally.
Partner-defined output
Prompts, labels, QA criteria, and formats are designed around the intended use.
Why playable collection
Not another labeling queue.
Clap is built to create missing human behavior, not only annotate existing files.
Traditional collectionClap Data Missions
Passive annotationActive human performance
Static uploaded assetsPrompt-specific data created on demand
One-dimensional labelsVideo, voice, choice, timing, and explanation
Worker completes a taskParticipant plays an engaging mission
Mission concept preview
Save One turns preference into explainable human data.
Participants see four choices, save one, then explain their decision on camera. One short mission can produce the selection, response latency, video, voice, transcript, reasoning, and quality signals.
Camera version in development with design partners
Discuss this mission
Illustrative interactionSelect a capability to save it
You can keep only one AI capability. Which one survives?
Camera responseConcept preview
Selected · Voice
“I would save Voice because it removes the friction of stopping, typing, and looking at a screen.”
choicelatencyvideoaudiotranscript
Illustrative pilot blueprint
From model gap to a scoped mission.
This is an example design, not a completed customer engagement.
Partner question
Which features, products, interfaces, or model responses do people prioritize, and what reasoning drives the choice?
Mission
Show four options. Ask the participant to save one and explain the decision naturally on camera.
Illustrative scope
250 accepted adult responses, with cohorts and acceptance criteria defined with the partner.
Deliverables
Choice event, time-to-choice, MP4/WAV, transcript, explanation labels, QA status, and provenance fields.
Acceptance checks
Prompt completion, audible explanation, usable framing, single participant, integrity review, and required metadata.
Rights design
Mission-specific disclosure, versioned consent record, and defined use scope, finalized around partner requirements and counsel review.
Initial focus
Three data problems that fit Clap naturally.
Multimodal evaluations + human baselines
Collect human attempts, reactions, and judgments against tightly specified tasks and rubrics to benchmark model behavior.
Instruction, gesture + behavioral understanding
Capture how people follow instructions, gesture, demonstrate, and move through physical tasks on camera.
Spontaneous speech, choices + explanations
Elicit natural speech, emotional prosody, reactions, decisions, and the reasoning behind them.
The same mission formats also serve consumer-insight teams that need explained preference at scale — an adjacent application of the identical pipeline.
Illustrative output
Raw human signal. Structured around the intended use.
The schema below is a product design example, not a completed buyer dataset.
- Video and optional isolated audio
- Choice, timing, and interaction events
- Verbatim transcript and explanation labels
- Acceptance status and rejection reasons
- Versioned provenance and permitted-use scope
| Submission | Choice | Signals | QA | Explanation excerpt |
|---|---|---|---|---|
| sub_demo_001prompt_v1.1 | Voice1.84s latency | video · voice · choice · timing | Accepted | “It removes the friction of stopping and typing...” |
| sub_demo_002prompt_v1.1 | Memory3.12s latency | video · voice · choice · timing | Accepted | “I do not want to repeat my context every time...” |
| sub_demo_003prompt_v1.1 | Vision2.43s latency | video · voice · choice · timing | Review | “Physical-world context opens a different class of help...” |
Rights, provenance, and QA
Designed to make every record explainable.
Before collection begins, Clap will define mission-specific disclosure, permitted use, retention, acceptance criteria, and buyer-delivery terms with the partner and counsel.
Mission-specific disclosure
The mission disclosure will state what is collected and the intended use before a participant submits.
Versioned provenance
The proposed record format includes the prompt version, capture metadata, and consent-record reference.
Partner-defined acceptance
Acceptance checks and rejection reasons are defined around the behavior, modality, and downstream use.
Scoped rights design
Evaluation, research, and training permissions are scoped separately rather than bundled into one vague grant.
Data Missions is currently in the design-partner stage. No pilot collection or licensing begins until rights and delivery terms are reviewed with the partner and counsel.
Co-design the first missions
What human behavior does your model misunderstand?
Bring the data gap. Clap will help turn it into a mission, draft the response schema, and pressure-test the pilot with you.