Caplo AI
FreeDiscover Caplo AI by Sparklight—an AI-powered tool for repurposing long videos into short clips with captions, subtitles, and social-ready formats.
About Caplo AI
Caplo is an iOS mobile application developed by Sparklight that provides live translated captions for other iPhone apps. It listens to app audio—such as live streams, anime, sports commentary, podcasts, courses, and news—and displays real-time transcriptions and translations in a floating Picture-in-Picture (PiP) window. The tool supports 12 languages and aims to help users follow content when native subtitles are missing, delayed, or in an unfamiliar language. Caplo also offers session history with iCloud sync and Markdown export for later reference. The app focuses on enhancing accessibility and understanding for a wide range of audio-based content.
Key Features
Pros & Cons
- Works across any iPhone app via a floating PiP overlay without per-app integration
- Offers real-time transcription and translation between 12 languages
- iCloud sync and Markdown export provide convenient record-keeping
- Simple pricing appears to include a subscription option and pay-as-you-go credit packs
- Appears to be currently limited to iOS (iPhone) only; Android support should be verified
- Requires a stable internet connection for live translation functionality
- Free tier availability and its limits are not clearly described on the website
- Performance may vary with strong accents or fast speakers, as acknowledged by user feedback
- Subscription minute cap (600 min/month) may not suit heavy users
Best For
Alternatives to Caplo AI
Pix2Pix Video
AI-Powered Image-to-Video Conversion: Pix2Pix-Video
Plazma Punk
Turn any song into a visually stunning music video with Plazma Punk’s AI-driven platform. Perfect for artists, podcasters, and digital storytellers.
Rask.ai
Scale intelligent video localization using Rask.ai
Visla
Visla: AI Video Generator and Editor Designed for Business Teams
Stable Video Diffusion
AI video generation from images and text
DreaMoving
A Human Video Generation Framework based on Diffusion Models