Clipto - Fully local, natural language search over terabytes of media

@amazing_1 Thanks, really glad you like the UI. We currently support transcription in 99+ languages, so it should work well for multilingual audio and video content across different workflows

@jay_gangwar Good question! That’s fair — cloud storage can be convenient, especially if you want everything synced across devices. But for a lot of filmmakers, editors, and creators, the problem is that raw footage is huge and often sensitive. Uploading terabytes of media can be slow, expensive, and not always something people are comfortable with. Clipto is built for the other workflow: your media already lives on your local drives, and the AI helps you make it searchable without uploading everything first. So it’s not really “cloud vs local” for everyone. If you prefer cloud storage, that can still work for you.:)

@limxn6 High praise — thank you. 😊

Apple Photos is great for memories. Clipto is built for work: terabytes of raw footage, interviews, production assets — all searchable locally, offline, instantly.

Glad it resonates. Let me know what you find when you try it.

@rocsheh Thanks, Zepeng!

Yes, Clipto already supports this today!

You can assign custom names to detected faces, so instead of searching for “a person”, you can search for people that actually matter to you, such as family members, friends, clients, or collaborators.

We’ve found that once people start organizing media around real identities, search becomes much more powerful. Instead of “find a woman speaking on stage,” you can search for things like “Mom’s speech”, “Client A interview”, or “John at the conference.”

We think that’s an important step toward turning media search into a true personal memory system.

@sabber_ahamed That's a great question.

First, choose a smaller model.

Second, slim it down through optimization.

Finally, schedule tasks flexibly based on how busy your computer is — that is, 'model miniaturization itself, compression optimization, and flexible task scheduling based on the user's machine usage.

@dhanishta_likhar That’s a very insightful observation.

We actually agree with your premise. For local AI products, you have to solve the hardest problem first. If you can’t make large-scale on-device indexing practical, everything else is just a demo.

As for indexing, it’s incremental. Once your library has been processed, Clipto only analyzes newly added or changed files. It doesn’t re-chew the entire archive every time.

A lot of our engineering work has gone into task scheduling, indexing pipelines, and resource management to make sure growing libraries remain practical over time.

That’s ultimately the difference between a product that works for a 50GB library and one that can keep scaling as your archive grows year after year.

@michael_zorez Yes, absolutely.

While many of our examples come from professional media workflows, this is actually a problem we think about a lot.

In many ways, a family archive is even harder than a professional one. You have photos and videos scattered across phones, cameras, external drives, cloud folders, and years of inconsistent naming conventions.

Clipto is designed to index the content itself, not just filenames or metadata. So even when metadata is missing or incomplete, it can still use visual content, people, dialogue, scenes, objects, and other signals to make the library searchable.

That’s why we’re particularly interested in the “messy archive” use case. Most people don’t have a well-organized media library. They have exactly what you described: tens of thousands of files accumulated over years.

When you’re trying to find something in that archive, what are the searches you most wish you could do today but can’t?

@mona_xx That’s exactly the problem we built Clipto for. 😄

Too often we know we have the clip somewhere, but finding it means opening folder after folder. Glad Clipto saved you an hour today!

@kjlis Great questions！

For dialogue search, we support 100+ languages through our speech recognition pipeline, including English, French, Italian, Spanish, Japanese, Chinese, and many others. As long as the language is supported by the underlying ASR models, the dialogue becomes searchable. Accuracy can vary by language, audio quality, accents, and recording conditions, but we’ve found it works very well across most major languages.

For compound queries, yes. We don’t treat search as simple keyword matching. We use semantic retrieval and reranking to understand the intent behind a query. For something like:

“Find clips that contain both X and Y”

clips matching both concepts would typically rank highest, while clips matching only X or only Y may still appear further down the results if they are semantically relevant. In practice, the system tries to optimize for the user’s intent rather than applying strict boolean logic.

We’d love to hear more about the workflows you’re thinking about. This is an area we’re actively improving.

@nithin_raju1 Yes, it does. Clipto supports video transcription search. Beyond transcription, AI can also generate a summary for your video. You can click into any single file to open its detail page, where you’ll see the transcript, summary, and a chat box. From there, you can ask questions about the video, and the AI will answer based on the content of that specific file：）