Glossary
Key terms used across AutoPersonas and AI-influencer operations. For deeper detail, see the documentation.
- AI influencer
- A synthetic social-media persona — a consistent character with a fixed face, body, styling, and voice — operated to publish content and engage an audience across platforms.
- Persona
- The structured identity of an AI influencer: appearance, personality, wardrobe, aesthetic, and voice. In AutoPersonas a persona is more than a system prompt — it's a structured record that drives generation.
- Profile
- The publishing identity layer. Every content item, social account, and analytics snapshot is profile-scoped. A profile may be backed by an AI character or run as a managed (real) account.
- Reference sheet
- A multi-view composite image (front, side, back, face) that locks a persona's identity so its appearance stays consistent across thousands of generated images and videos.
- Identity lock
- Holding a persona's face, body, wardrobe, and aesthetic steady across every generation, platform, and month of operation — the core problem AutoPersonas solves.
- Managed account
- A real brand or person account operated through AutoPersonas' publishing, captioning, scheduling, and analytics surface — without generating an AI persona.
- LoRA
- Low-Rank Adaptation — a lightweight fine-tuning technique that trains a small adapter on a persona's reference images to further improve likeness in image generation.
- Voice lock
- Keeping captions and replies consistent with a persona's established tone and personality, so written output reads unmistakably as the same character.
- Engagement engine
- Automated, voice-consistent replies and outbound discovery that grow and maintain a persona's audience without manual posting.
- Learning loop
- The continuous-learning pipeline that watches which posts land (engagement, reach) and biases future generations toward what performed well, by tuning per-tag and per-time weights that feed back into content generation.
- Cross-persona analytics
- Roster-level analytics that aggregate engagement across every persona an operator runs, with a per-persona breakdown — distinct from single-profile stats.
- A/B testing
- Defining control and test content variants, measuring each arm's real engagement, and declaring a winner by lift once both arms reach a minimum sample size.
- Usage-based billing
- Paying for the generation and publishing you actually use, billed against a card on file (pay-as-you-go) or rolled onto a Pro subscription invoice.
- MCP (Model Context Protocol)
- An open protocol that lets AI agents call tools on a server. AutoPersonas exposes its full product surface over a hosted MCP server so agents can operate personas programmatically.
- Collaboration studio
- Tooling for multi-persona collaborations and branded shoots, where two or more characters appear together consistently.