cini

What to watch.

Found.

Mood-Inspired discovery

Tell us how you feel. We curate exactly what matches your mood.

Effortless experience

Found your movie? Launch it on your TV with one tap— no casting or AirPlay, no searching, no friction.

Not ready to watch? Save it to your smart watchlist for later

Clever suggestions

Cini grasps your taste, habits, and mood to deliver the perfect movie or show in seconds

Kini grasps your taste, habits, and mood to deliver the perfect movie or show in seconds.

how it works

The Cinephile DNA™: High-Dimensional Metadata Layer
Instead of simple genre tags, Cini utilizes a 12-metric proprietary rubric to quantify the soul of a film. Every title in our 100k+ Golden Catalog is scored on a continuous 1.0–10.0 scale across dimensions like Narrative Complexity, Arousal Level, and Dialogue Density. We utilize deterministic LLM scoring with a 0.0 temperature and grounded "Anchor Movies" to ensure a consistent, drift-free data layer
Multi-Modal Neural Matching
To achieve a sub-45-second Decision to Play, our engine projects both user preferences and film DNA into a high-dimensional latent space. We utilize a distributed Vector Search Infrastructure to perform real-time mathematical queries. By calculating the distance between a user’s current vibe and our 100k+ title embeddings, we retrieve the most semantically relevant matches with near-zero latency, effectively solving the discovery paradox through pure geometry.
Reinforcement Learning & Explainable AI
Cini builds user trust through Explainable AI, providing a Micro-Reason for every suggestion based on historical embeddings or specific DNA matches (e.g., "A rare slow-burn with high character depth"). Behind the scenes, we utilize a Reinforcement Learning feedback loop. The algorithm re-ranks results by analyzing the gap between predicted and actual behavior—treating Watchlist additions and Dismissals as high-intent signals to refine the user's mathematical preference profile.
The Cinephile DNA™: High-Dimensional Metadata Layer
Instead of simple genre tags, Cini utilizes a 12-metric proprietary rubric to quantify the soul of a film. Every title in our 100k+ Golden Catalog is scored on a continuous 1.0–10.0 scale across dimensions like Narrative Complexity, Arousal Level, and Dialogue Density. We utilize deterministic LLM scoring with a 0.0 temperature and grounded "Anchor Movies" to ensure a consistent, drift-free data layer
Multi-Modal Neural Matching
To achieve a sub-45-second Decision to Play, our engine projects both user preferences and film DNA into a high-dimensional latent space. We utilize a distributed Vector Search Infrastructure to perform real-time mathematical queries. By calculating the distance between a user’s current vibe and our 100k+ title embeddings, we retrieve the most semantically relevant matches with near-zero latency, effectively solving the discovery paradox through pure geometry.
Reinforcement Learning & Explainable AI
Cini builds user trust through Explainable AI, providing a Micro-Reason for every suggestion based on historical embeddings or specific DNA matches (e.g., "A rare slow-burn with high character depth"). Behind the scenes, we utilize a Reinforcement Learning feedback loop. The algorithm re-ranks results by analyzing the gap between predicted and actual behavior—treating Watchlist additions and Dismissals as high-intent signals to refine the user's mathematical preference profile.
The Cinephile DNA™: High-Dimensional Metadata Layer
Instead of simple genre tags, Cini utilizes a 12-metric proprietary rubric to quantify the soul of a film. Every title in our 100k+ Golden Catalog is scored on a continuous 1.0–10.0 scale across dimensions like Narrative Complexity, Arousal Level, and Dialogue Density. We utilize deterministic LLM scoring with a 0.0 temperature and grounded "Anchor Movies" to ensure a consistent, drift-free data layer
Multi-Modal Neural Matching
To achieve a sub-45-second Decision to Play, our engine projects both user preferences and film DNA into a high-dimensional latent space. We utilize a distributed Vector Search Infrastructure to perform real-time mathematical queries. By calculating the distance between a user’s current vibe and our 100k+ title embeddings, we retrieve the most semantically relevant matches with near-zero latency, effectively solving the discovery paradox through pure geometry.
Reinforcement Learning & Explainable AI
Cini builds user trust through Explainable AI, providing a Micro-Reason for every suggestion based on historical embeddings or specific DNA matches (e.g., "A rare slow-burn with high character depth"). Behind the scenes, we utilize a Reinforcement Learning feedback loop. The algorithm re-ranks results by analyzing the gap between predicted and actual behavior—treating Watchlist additions and Dismissals as high-intent signals to refine the user's mathematical preference profile.
The Cinephile DNA™: High-Dimensional Metadata Layer
Instead of simple genre tags, Cini utilizes a 12-metric proprietary rubric to quantify the soul of a film. Every title in our 100k+ Golden Catalog is scored on a continuous 1.0–10.0 scale across dimensions like Narrative Complexity, Arousal Level, and Dialogue Density. We utilize deterministic LLM scoring with a 0.0 temperature and grounded "Anchor Movies" to ensure a consistent, drift-free data layer
Multi-Modal Neural Matching
To achieve a sub-45-second Decision to Play, our engine projects both user preferences and film DNA into a high-dimensional latent space. We utilize a distributed Vector Search Infrastructure to perform real-time mathematical queries. By calculating the distance between a user’s current vibe and our 100k+ title embeddings, we retrieve the most semantically relevant matches with near-zero latency, effectively solving the discovery paradox through pure geometry.
Reinforcement Learning & Explainable AI
Cini builds user trust through Explainable AI, providing a Micro-Reason for every suggestion based on historical embeddings or specific DNA matches (e.g., "A rare slow-burn with high character depth"). Behind the scenes, we utilize a Reinforcement Learning feedback loop. The algorithm re-ranks results by analyzing the gap between predicted and actual behavior—treating Watchlist additions and Dismissals as high-intent signals to refine the user's mathematical preference profile.