For AI Platforms
Trusted short-form media for LLMs and agents
Consume licensed Swivve Objects with video, transcripts, source links, verification labels, correction history, provenance, and accountable author metadata.
Machine-readable object
Each Swivve includes structured fields for retrieval and audit
Video asset access mode
Transcript
Canonical written story
Source bundle
Claims and entities
Status label
Correction timeline
Origin label
Author type
Rights and usage policy
Risk posture
For consumption, not redistribution by default
Start with inference-first access and explicit controls before any broader usage rights.
What access supports
Inference use, source-aware retrieval, citation-required outputs, bounded caching, auditability, and takedown propagation.
What you do not get by default
No raw commercial redistribution rights, no training rights, no white-label republishing, and no silent correction drift.
Signal Profile · Intended schema
Short-video integrity context that machines can inspect
The pre-release Signal direction is intended to let approved systems receive structured risk, evidence, uncertainty, policy, and review context before retrieving, recommending, citing, or redistributing video.
Safety state
Identity risk
Synthetic-media state
Disclosure state
Claim status
Attention-design risk
Confidence
Evidence segments
Applicable policy
Review state
Intended architecture only; no production Signal endpoint is available today.
Use cases
Designed for high-trust AI applications
Answer engines
Provide short-form news answers grounded in licensed sources with citation-aware outputs.
Monitoring and research copilots
Track developing stories, correction events, and source updates with machine-readable state changes.
Newsroom assistants and internal analysts
Use bounded, rights-aware retrieval to support editorial workflows without silent correction drift.