Introducing the Clipperia Video Intelligence API

Clipperia watches video for creators. It goes through podcasts, streams, webinars and interviews, works out what happens in them, and finds the moments worth clipping.

The clipping is what creators see. Underneath it is the harder part: understanding what was said and shown in a video, and when. Plenty of products need that understanding without the clipping, and without building the machinery to get it.

Today we're opening the waitlist for the Clipperia Video Intelligence API. It turns that understanding into something your product can call.

The short version

1. Send your video once.
2. Ask it anything in plain language. For example, "list every product mentioned and when", "where does the speaker talk about pricing?" or "is anything in here unsafe for a family audience?"
3. Get back structured, timestamped answers in the exact fields you asked for, ready for your product to use.

That's the whole idea. You don't write a pipeline, babysit a model or build a queue. Your team works on the feature, not the plumbing.

Why we built it

Adding video understanding to a product sounds like one feature. In practice it turns into a project that never ends. Before you can ask a video a single question, someone on your team has to:

• Get large files in reliably and process them without timing out
• Turn hours of footage into something a model can actually reason about
• Pick a model, write prompts, and then fight its context limits on long videos
• Turn loose model output into data your code can trust
• Handle retries, failures, duplicates and billing surprises
• Rebuild part of all that every time the model underneath changes

We know because we built all of it to run Clipperia. Most teams that try end up shelving the feature, or shipping a fragile version that only works on short clips. The Video Intelligence API puts all of that behind a single, simple request. That work becomes our job instead of yours.

What you can ask

If a person could answer it by watching the video carefully, you can ask it. Some examples:

• Course platforms: "Split this lesson into chapters with titles and start times."
• Podcast networks: "Find every sponsor read: sponsor name, start, end, and whether the promo code was said."
• UGC apps: "Flag any segment that's unsafe to publish, with the reason and the timestamp."
• Media teams: "Pull out every factual claim the guest makes so an editor can check it."
• Sales and support: "What objections did the customer raise, and how did the rep respond?"

Every answer points back to the moment it came from, so your product can jump straight to it.

Answers in your shape

A paragraph of text is easy to get from a model. Getting data your code can rely on is the hard part.

With the Video Intelligence API, you describe the fields you want back, like names, timestamps, categories and scores, and that's what comes back every time. You don't have to scrape JSON out of a chat reply or write a parser that breaks the next time the model changes.

Send once, ask many times

The heavy lifting happens once, when your video arrives. After that, each new question is just a question. It doesn't mean re-uploading, re-processing or paying to handle the whole file again.

That changes what you can build. You can ask a new question about last month's videos without starting over. You can let your users ask their own questions about a video, because each one is cheap and fast.

Two kinds of questions, two prices

Most questions can be answered from what we gather when your video is first uploaded. These standard analyses are the fast, inexpensive default.

Some questions need a closer look at very specific details on screen. For example, "what's written on the whiteboard at 12:40?" or "which brand logo is on the speaker's shirt?" These visual analyses cost more, because they need more work.

You pay only for what runs, and you're billed on the video you actually sent. If something fails on our side, you don't pay for it. Exact prices will be published before launch.

What it won't do

We'd rather be upfront:

• It doesn't recognize faces or identify people by their appearance.
• It doesn't search across your whole library in one request. It answers questions about the video you point it at. (Many teams store the answers in their own search index. We cover that in our archive tagging guide.)
• It doesn't fact-check claims. It can pull them out for your editors, but it won't rule on whether they're true.
• It accepts MP4 files for now.

Who it's for

You don't need machine learning or video experience. If your product can send a file and read data back, you can use it. The teams we're building for first:

• Product teams who've had a video feature sitting in the backlog
• Platforms with large video catalogs they can't watch themselves
• Agencies and media companies that review video for clients
• Developers who tried to do this with a general-purpose model and hit its limits

How to get access

Access opens to the waitlist first, and we're letting people in gradually. When you join, tell us what you want to ask your videos. We read every answer, and the most concrete use cases get keys first.

Join the Video Intelligence API waitlist →

Want to see how it fits a specific job? Read how teams use it for brand-safety review, archive tagging and turning recorded calls into data. Or see how it compares with Google Video Intelligence, AWS Rekognition Video and Twelve Labs.