Create fire diss tracks with advanced AI technology
Enterprise video AI powered by multimodal intelligence for semantic search, analysis, and understanding
Stage
Not given
Established
Founded
Not given
2021
Based in
Not given
US
Pricing model
Freemium
Freemium
Pricing
Starter Pack: $5 USD (80 credits, create 4 diss tracks); Creator Pack: $20 USD (400 credits, create 20 diss tracks). Each diss track generation costs 20 credits. No subscription required, pay once and use forever.
Free plan: $0/month with 3 GB storage and 600 minutes of video indexing. Plus plan: $29/month with 50 GB storage. Pro plan: $99/month with 200 GB storage. Custom plans available. Annual billing offers 17% discount.
Key features
AI-powered diss track generation with clever wordplay and aggressive flows
Multiple diss track styles including trap, boom bap, and drill
Customizable beat and flow options with tempo and intensity control
Full editing capabilities for all generated diss tracks
Commercial usage rights for all generated content
Beat customization from extensive library
Semantic video search across entire libraries using natural language
Automatic content segmentation with scene change detection
Compliance and policy risk identification at scale
Automated highlight generation from video libraries
Video insight analysis and pattern detection
Fast indexing at ~60x real-time speed
API and SDK with multiple language support
Jockey AI agent for video search in Claude and ChatGPT
SOC 2 Type II certification and encrypted data handling
What makes it different
Fully editable AI-generated diss tracks that understand hip-hop culture and contemporary rap trends
Commercial rights included - use generated tracks on streaming, YouTube, TikTok and other platforms
No subscription required - pay once and use credits forever
Free credits for new users to explore the platform
Multiple customizable styles and flows for unique creative expression
Video-native foundation models trained specifically for video understanding
Processes vision, audio, speech, and on-screen text together in one index
Reasons across full temporal sequences up to two hours, not just frame sampling
Spatiotemporal embeddings make every video moment findable by actual content, not metadata
TwelveLabs