Auto-Tag and Index a Video Archive So People Can Find Any Moment

Every organization with video has the same problem. The library keeps growing, and nobody knows what's in it.

Media companies have years of interviews nobody can search. Course platforms have thousands of lessons with one-line descriptions. Companies have every all-hands, training session and customer call recorded, and none of them can be found. Tagging it all by hand would take someone months, and they'd fall behind again within weeks.

The Clipperia Video Intelligence API lets you tag an entire archive automatically. You decide what to capture, and every tag comes with the timestamp it belongs to.

What you end up with

For every video in your library, you get structured data that you store and search however you like:

• Chapters with titles, summaries and start/end times
• Topics and keywords people are likely to search for
• Named things: products, companies, places and subjects mentioned or shown
• Content labels such as tone, format, language and audience suitability
• Notable moments like quotes, announcements, demos and highlights

You choose the fields. A news archive might care about places and quotes. A course platform might care about concepts and difficulty. A brand team might care about product appearances. You ask for the shape that fits your search, and that's the shape you get back.

How it works

Step 1: Decide what "findable" means for your library

Before you tag anything, write down the searches you want to support. For example:

• "Every interview where someone talks about remote work"
• "Lessons that explain recursion, beginner level"
• "Videos where our new logo appears on screen"
• "All-hands segments about the 2026 roadmap"

Those searches tell you which fields to ask for.

Step 2: Send each video once

Send each file in your archive to the API. The heavy lifting happens once per video, when it arrives. You can work through a backlog of thousands of files without running a single video server, and you'll be notified as each one is ready.

Step 3: Ask your tagging question

Write the question in plain language, as you'd brief a researcher, and describe the fields you want back. For example:

"Split this video into chapters. For each chapter, give a title, a one-sentence summary, up to eight search keywords, any products or companies mentioned, and the start and end time."

Every video comes back in the same structure. Your code can trust it without cleaning it up first.

Step 4: Load the results into the search you already have

Store the answers in your database or search index: Postgres, Elasticsearch, Algolia, Typesense, or whatever you already run. The API doesn't replace your search. It gives your search something worth indexing.

Because every chapter and keyword carries a timestamp, each search result can open the video at the right moment instead of at the beginning.

Step 5: Tag new uploads as they arrive

Hook the same flow into your upload process. New videos are tagged automatically and become searchable without anyone touching them.

Changing your mind later doesn't mean starting over

Archives outlive the questions you first asked of them. Six months from now, you might want to add "speakers' job titles" or "on-screen text" to your index.

Because each video is processed once when it arrives, asking a new question of an existing archive is just another question. You don't re-upload or reprocess anything, and you don't pay to handle the whole file again. Your index can grow with your needs.

Standard vs. visual questions

Most tagging can be answered from what's gathered when a video is first uploaded. That covers topics, chapters, keywords, mentions and summaries. These standard analyses are the fast, inexpensive default, and they're the right choice for indexing a large archive.

When you need very specific details from what's on screen, use a visual analysis. Examples: "the text on every slide" or "which product is on the table." Visual analyses cost more, so most teams use them only on the videos or segments that need them.

Tips for a great archive index

• Keep keywords human. Ask for "words a viewer would search for," not technical labels. People search for "the budget talk," not "finance, meeting, indoor."
• Store summaries, not just keywords. A one-line summary in each result helps people pick the right moment quickly.
• Use a fixed list where it matters. If your library has set categories, include them in your question so every video uses the same ones.
• Start with a sample. Tag 50 representative videos, try real searches against them, and adjust your fields before running the whole archive.
• Keep the timestamps. They're what turns "found the video" into "found the moment."

What it doesn't do

The API answers questions about the video you point it at. It doesn't search across your whole library in one request. That's what your search index is for, and this workflow fills it with good data. It also doesn't recognize faces or identify people by their appearance. It can capture names that are spoken or shown on screen.

Who this is for

• Media and publishing teams making years of footage reusable
• Course and education platforms that want search inside lessons
• Internal comms and L&D teams buried in recorded meetings and training
• Agencies managing large client video libraries
• Product teams adding "search inside videos" as a feature

Get started

The Video Intelligence API is opening to the waitlist first. When you join, tell us about your archive, its size and what people need to find. That helps us decide who gets access first.

For the bigger picture on in-video search, read how to search inside videos without building a pipeline.