A Production Studio for Virtual Talent.
Exempora is production software for teams running AI influencer campaigns. Lock the persona. Approve the references. Check every asset. Review before publish. Track what works.
Studio / Operating System / Review & ControlsLocked backstory, voice, values, guardrails, and disclosure rules. The source of truth for who the influencer is and what they will not say.
Approved master references create the visual baseline. Every generated asset is checked against them for face, pose, color, and style match.
Every asset is approved or rejected before publish. Internal review, client approval, regeneration notes — all traced to a decision.
Random images do not build influence.
Characters need rules.
Campaigns need repeatable output.
Performance improves through loops.
AI influencers need production direction.
Every asset is scored against approved references before it ships.
Guardrails, forbidden topics, and tone limits are locked in the persona canon.
FTC-compliant disclosure rules are baked into every influencer profile.
Every asset is approved or rejected. Nothing publishes autonomously.
Approved master references create the visual baseline for consistency checks.
Qualitative learnings track what content performs, not fake analytics.
Likeness & Identity
Exempora does not create likenesses of real people without explicit consent. Every influencer is a fictional character with a defined persona canon. Brand safety guardrails prevent off-topic content, unauthorized endorsements, and deceptive practices.
Copyright & Ownership
All campaign assets generated through Exempora workflows are owned by the client. Prompt recipes, reference vaults, and persona canons are portable between workspaces and can be exported at any time.
FTC Disclosure
Disclosure rules are locked at the persona level. Every influencer has mandatory disclosure language and hashtag requirements. The review queue enforces these checks before any asset is approved for publish.
Data & Privacy
Reference vaults and campaign data are scoped to your workspace. No data is shared between workspaces or used to train external models. You control what stays and what goes.