If your team is sitting on thousands of hours of raw video, you’re not alone — and you’re probably not sleeping well, either.
From broadcasters to documentary producers, media companies today are sitting on decades of valuable footage. But as soon as that content gets digitized, another problem kicks in: how do you organize, search, and make sense of it all — without spending months tagging it manually?
That’s where AI-assisted metadata tagging enters the scene.
Table of Contents
The Problem: Manual Tagging Just Doesn’t Scale
Let’s say you’re digitizing your archive — tapes, drives, DVDs. Suddenly you’ve got THOUSANDS of hours of video, all dumped into storage.
Now what?
Manual tagging isn’t just time-consuming — it’s inconsistent, prone to human error, and expensive. Even with interns or freelancers, the cost of tagging just 100 hours of content quickly adds up. Multiply that across an entire archive and it’s clear: humans alone can’t handle the load.
The Solution: AI Tagging at Scale
Modern AI models can now tag, summarize, and transcribe video content with stunning accuracy — and they can do it automatically, at scale.
Using tools like:
- Whisper for speech-to-text transcription
- BLIP or CLIP for frame-based visual tagging
- X-CLIP for understanding entire scenes across time
- Gemini for high-quality multimodal summarization
You can build a pipeline that extracts meaningful, structured metadata from hours of footage — without ever hitting pause.
Frame-by-Frame vs Scene-Level: Why X-CLIP Matters
Most tagging systems work one frame at a time. They’ll tell you there’s a “person,” a “microphone,” a “plant.” That’s useful — but it’s shallow.
With X-CLIP, you go deeper.
Instead of analyzing isolated frames, X-CLIP ingests multiple frames across time — usually 8 to 16 — to understand full sequences. That means it can tag actions, transitions, and real context like:
“A presenter walks on stage and begins a product demo.”
That’s the level of understanding that transforms raw footage into searchable, structured knowledge.
Cloud vs Local: Build the Workflow That Fits Your Needs
Some teams want the simplicity of cloud APIs like Gemini or OpenAI. Others need everything offline — for privacy, speed, or compliance.
That’s why the best metadata systems today are modular and hybrid:
- Use cloud APIs for tasks like summarization or NLP
- Run local models like Whisper and X-CLIP for tagging, transcription, or batch jobs
- Combine everything in a pipeline that outputs CSV, JSON, XML, or XMP — ready for import into your DAM or CMS
You’re not buying a tool. You’re designing a system that fits your organization’s scale.
Real-World Example: 17,000 Hours of Footage, One Pipeline
One of the companies we’re working with recently kicked off a major digitization project — 17,000 hours of footage across formats.
They needed a way to:
- Tag every clip with consistent, searchable metadata
- Extract summaries and transcriptions
- Output the data in a format compatible with their internal content systems
We helped them architect a pipeline that uses frame sampling, X-CLIP tagging, Whisper transcription, and smart summarization to process files in batches — automatically.
This system runs on their machines, in their environment — no vendor lock-in, no bandwidth bottlenecks.
Want to Test It?
If you’re just starting out, we offer a prosumer-level tagging app called VideoTagger — perfect for smaller teams and early experimentation.
But for enterprise workflows, custom integration is where we shine. If you’re managing a large archive or planning a digitization initiative, let’s talk.
We’ll help you turn hours of raw footage into an asset that actually works for you.
Curious to try it yourself?
You can start with VideoTagger, our prosumer-friendly tool for small-scale tagging.
Need something built for scale?
We offer enterprise-grade integrations and custom pipelines tailored to your team’s workflow. Let’s talk shop.
