Human Monitoring is Expensive, Exhausting, and Doesn’t Scale

From baby monitors to warehouse cameras and endless CCTV feeds, many organizations still rely on human eyes to monitor live video—a costly, tedious, and error-prone approach. Fatigue inevitably sets in, accuracy drops, and scaling up means hiring more personnel instead of enhancing systems. In an AI-driven era, monitoring shouldn’t be manual, prone to mistakes, or become a bottleneck.

Imagine receiving instant alerts the moment a package is stolen, a baby attempts to climb out of a crib, a flood risk emerges, or a doctor needs to know about a critical patient event—all in real time. This is the promise realized by VideoDB’s real-time infrastructure, now natively integrated with TwelveLabs and our most advanced video-understanding model, Pegasus 1.2.

Huge thanks to the VideoDB team (Ashutosh Trivedi, Ashish Choithani, Om Gate, and Nischay Joshi) for collaborating with me on this integration!

Live Video to Instant Action

Real-time video analysis isn’t just a “nice-to-have”—it’s a transformational capability:

• Safety and Security: Move from reactive security to proactive, potentially life-saving alerts during emergencies or security breaches.

• Enterprise Productivity: Convert passive video archives into interactive, searchable knowledge, unlocking collaboration and insights.

• Content Platforms: Enable automatic tagging, chaptering, and precise moderation for a better user experience.

Yet, technical hurdles have kept most development teams from unlocking this potential—until now.

The Challenge: Building Video AI is Hard

If you’ve tried to build real video understanding, you know the familiar headaches:

• API Sprawl: Managing countless credentials, rate limits, and disjointed SDKs.

• Scaling Woes: Resource-heavy GPU workloads make it tough to scale reliably.

• Latency Bottlenecks: Juggling storage, AI, and application latency undermines true real-time use.

These challenges often stall innovation just when your ideas are ready for launch.

VideoDB + TwelveLabs: Developer Simplicity, Enterprise Power

VideoDB was built specifically to remove these obstacles. Developers get an AI-first platform for video data management: seamless ingestion, customizable indexes, multi-stream processing, and advanced event alerting—all behind a single, unified API. Best of all, with the new native TwelveLabs integration, the world’s most advanced video understanding AI is at your fingertips, fully embedded within VideoDB.

No extra accounts. No additional API keys. Zero integration headaches.

Just specify the model—twelvelabs-pegasus-1.2—for instant access to state-of-the-art video understanding in your existing workflow.

Real-World Use Cases: Video AI in Action

🌊 Flash Flood Detection

Imagine a camera monitoring a flood-prone riverbed. With TwelveLabs in VideoDB, you can continuously spot the tiniest signs of trouble. The moment Pegasus detects rising waters, VideoDB’s alerting system triggers life-saving responses—automatically.

Open the sample notebook

👶 Baby Crib Monitoring

Parents deserve restful, worry-free sleep. With TwelveLabs-powered real-time monitoring, you’ll instantly know if your baby tries to climb out or needs urgent attention. VideoDB + TwelveLabs means peace of mind—minute by minute.

Open the sample notebook

How It Works: Advanced Video Understanding, One Line Away

It’s easy to index a video stream with TwelveLabs Pegasus in VideoDB. Here’s how it looks like in the flash flood detection notebook:

from videodb import SceneExtractionType

# Your stream object
flood_stream = ... 

# Index scenes using TwelveLabs' Pegasus model
flood_scene_index = flood_stream.index_scenes(
    extraction_type=SceneExtractionType.time_based,
    extraction_config={
        "time": 10,
        "frame_count": 6,
    },
    # This prompt guides the AI to look for events of interest:
    prompt="Monitor the dry riverbed and surrounding area. If moving water is detected across the land, identify it as a flash flood and describe the scene.",
    # Unlock the full power of TwelveLabs video intelligence here:
    model_name="twelvelabs-pegasus-1.2",
    name="Flash_Flood_Detection_Index"
)

print("Scene Index ID:", flood_scene_index.rtstream_index_id)

• model_name: Instantly opt into Pegasus’s video understanding with "twelvelabs-pegasus-1.2".

• extraction_type: Choose between time-based, scene-based, or custom analyses.

• prompt: Steer the AI to focus on your use case and generate actionable outputs.

You can see another example in the baby crib monitoring notebook:

from videodb import SceneExtractionType

# Your stream object
crib_stream = ... 

# Index scenes using TwelveLabs' Pegasus model
crib_scene_index = crib_stream.index_scenes(
    extraction_type=SceneExtractionType.time_based,
    extraction_config={
        "time": 10,
        "frame_count": 6,
    },
    prompt="Describe the activity of the baby kept inside a baby crib. Notice if baby climbs out, is stading up in the crib or attempts to escape.",
    name="Baby_Crib_Index",
    model_name="twelvelabs-pegasus-1.2"
)

crib_index_id = crib_scene_index.rtstream_index_id
print("Scene Index ID:", crib_index_id)

This unlocks seamless, instant integration of TwelveLabs intelligence—no friction, no extra steps.

Ready to build?

• Get started today:

  • Visit the VideoDB doc + TwelveLabs documentation

  • Read this detailed README

  • Join the TwelveLabs developer hub to share your own builds!

• Have questions or want to showcase your project? Reach out on our Discord!

We can’t wait to see what you create!