Video Clip Library's answer

One-Time Payment

$11 for a perpetual license. No subscriptions, no cloud storage fees. Just desktop software that keeps working on your computer.

Virtual Clips with Metadata

Instead of creating duplicate files, mark specific regions in your videos and attach searchable metadata to those regions. Want to find all clips where someone mentions "product launch"? Search across all your videos using this metadata. Works even when drives are disconnected.

• Edit metadata.
• Organize assets using collections, and find assets using powerful filters and advanced metadata search features.
• Add keywords, labels, and ratings to assets.
• Skip through clips at 16x speed to find the good parts
• Sync multiple camera angles automatically - create a clip from one camera and instantly view the same moment from other angles
• Export virtual clips directly to your NLE (Final Cut, Premiere, etc.)

Automation for moving video files around

You have videos scattered across multiple hard drives. No more manual file management or remembering which clips are on which drive. Build a shopping list of clips you need, and the software automatically handles the export jobs. Just plug in drives one at a time - the software finds the files, moves them where they need to go, and tells you when to switch to the next drive.

Multicam Support (for documentary, film, and law enforcement)

Link multiple video files from different cameras together. Create a virtual clip and instantly switch between camera angles. Preview rough cuts with built in video player. Export multicam clips directly to your NLE with all angles synchronized. Perfect for reviewing body cam footage, interviews, or any multi-camera production.

TranscriptFetch's answer:

Most short-form video has no caption track to download. TikTok’s auto-captions are opt-in per upload, Instagram never publishes a downloadable track, and many captions on both are burned into the video frames where no parser can read them. TranscriptFetch transcribes the audio when no caption track exists, on the same endpoint, with the same response shape. Your code never branches on which method produced the text. It also covers YouTube, TikTok, Instagram, X and Facebook plus any web page as clean Markdown, so a pipeline spanning several sources is one integration rather than five.

TranscriptFetch's answer:

Three reasons. Coverage: one API key and one response shape across five video platforms and the open web, instead of stitching together a library per platform. Reliability: requests run through rotating infrastructure, so code that works locally keeps working from a server, which is where most open-source approaches break. Billing that matches reality: one credit per successful response, with failed, blocked and empty results never charged. That last point matters on short-form video, where a meaningful share of any batch is music with no speech in it. There is also an MCP server, so AI agents can fetch transcripts as a tool without a custom integration.

TranscriptFetch's answer:

Developers and technical teams building on video and web content. The common cases are RAG and retrieval pipelines that need video as text, AI agents that need to read a link mid-conversation, content teams repurposing short-form video at scale, and media monitoring and research tools. It is an API first, so the buyer is usually the person writing the integration rather than an end user. The free browser tools exist for one-off transcripts and for evaluating output quality before writing any code.

TranscriptFetch's answer:

It started with discovering there is no good way to get the text of a video. YouTube’s official Data API will confirm a caption track exists and then refuse to hand it over, because captions.download requires the video owner’s OAuth token. The popular open-source libraries work until you deploy them, at which point platforms start refusing datacenter IPs. And YouTube is the easy case: TikTok and Instagram publish no caption file at all. Every workaround solved one platform, worked locally, and broke in production. TranscriptFetch is the version that handles the failure cases as first-class behaviour rather than edge cases.

TranscriptFetch's answer:

Next.js with TypeScript and Tailwind on the front end and API layer, Clerk for auth with SHA-256 hashed API keys, Neon Postgres with Drizzle ORM, Redis for caching, and Stripe for billing. The extraction layer is a Python and FastAPI service. Speech-to-text uses Whisper-class models. The MCP server is published in the official Model Context Protocol registry with a DNS-verified namespace.