What is Gaard?

Gaard turns surveillance video into decisions. This page gives you the five-minute picture of what the platform does, how it is organized, and how you interact with it.

The problem Gaard solves

Security and surveillance operations generate far more video than any team can watch. A single site can trigger thousands of motion alarms a day, the overwhelming majority of them harmless: wind in a tree, rain on a lens, an animal crossing a car park. Operators drown in false alarms, and the one event that matters is buried in the noise.

Gaard applies AI to that stream. It watches each clip, scores what it sees, and assigns a risk level so operators can spend their attention on the alarms that are actually suspicious. Harmless activity is filtered down; genuine intrusions are surfaced and escalated.

Two solutions

Gaard is sold as two entitleable solutions. Your account may include one or both.

Solution	What it delivers
Classify	AI video classification. Each clip is analyzed and assigned a risk level (safe, danger, or intrusion), a set of labels, and an annotated highlight showing what the model reacted to. This is the focus of these guides.
Replay	A cloud-based network video recorder (NVR) for recording and reviewing historical footage.

Note

These guides document Classify. Replay is available separately and is not yet covered in the public documentation.

The rest of this page (and most of the documentation) is about Classify.

How you interact with Gaard

Classify exposes the same data and the same vocabulary through three surfaces. Pick whichever fits the task.

Surface	Where	Use it to
Web app	app.gaard.ai	Review classifications, manage sites and cameras, configure the platform, and administer users. Start with the Web App Quickstart.
REST API	https://vision.gaard.ai/api	Submit video for classification and retrieve results programmatically. Start with the API Quickstart.
Outbound integrations	Your systems	Receive results automatically over webhooks, SFTP, or an alarm platform. See Webhooks.

All three read and write the same tenant. A clip submitted through the API appears in the web app, and a result delivered to your systems matches what an operator sees.

From video to decision

Every clip follows the same journey, whichever surface starts it.

1. Ingest. A video clip enters Gaard: uploaded in the web app, posted to POST /api/classify, or captured from a connected camera. Optional metadata identifies the site and camera it came from.
2. Classification. The AI model analyzes the clip and produces a set of confidence scores between 0.0 and 1.0: one per concept it looks for (intrusion, person, vehicule, animal, and more).
3. Risk level. Gaard derives a single risk level from the intrusion score using two configurable thresholds: below the low threshold is safe, between the two is danger, above the high threshold is intrusion. See Risk, labels, and scores.
4. Review. Operators open the classification in the web app, play the annotated clip, and confirm or correct the model’s decision. safe clips can be filtered out; danger and intrusion always stay visible.
5. Delivery. The result (status, risk level, labels, scores, and an annotated highlight) is available immediately over the API and can be pushed to your own systems by webhook, SFTP, or alarm platform.

The result of that journey is a small, structured decision instead of an hour of footage: this clip, from this camera, is an intrusion; here is the highlight.

Next steps

• Web App Quickstart: sign in and review your first classification.
• Core Concepts: the vocabulary shared across every surface.
• Risk, labels, and scores: how a score becomes a risk level.