u/Ilove_Cakez

AI has spent the last few years learning how to read, but it has remained essentially blind to video content. If you want to "search" a video today, you're still relying on human written captions or tags to tell the AI what it's looking at.

Our team wanted to build a Vision Layer for the AI stack.

Instead of just scraping metadata, we’ve built infrastructure that allows the AI to "watch" and "listen" to videos exactly like a human would! This means indexing the actual content inside the frame: the logos on a shelf, the spoken words in a routine, and the background context that never makes it into a hashtag.

The Use Cases we're solving for:

1. Searchable Video: Searching millions of videos by what is actually said or shown.
2. Competitive Intelligence: Seeing every brand mention (tagged or untagged!) across the platforms like TikTok or Instagram.
3. Modular Analysis: Taking that raw video data and using it to fuel your own workflows.

We’ve moved away from the Enterprise model of closed and expensive dashboards. We think video intelligence should be a modular utility.

We’d love to get some stress tests from this community. What’s a specific niche or competitor you’ve struggled to track? Let’s see if our indexing can find it (spoiler alert: it can)

If you rely on hashtags and @mentions to track your brand’s impact, your data is fundamentally incomplete.

Most social listening tools are text-based. They read captions but are blind to the actual video content. If a creator mentions your product in a tutorial or wears your brand in a routine but doesn't tag you, that reach is effectively invisible to your reporting stack.

We're the only ones calling this Shadow Reach (oriane.xyz/shadow-reach). Let's discuss the next big thing in marketing.

The Data:
On average, 76% of brand video exposure lives in what creators say and show, not in what they tag. In a recent audit for the brand Grüns, legacy tools found 117 mentions. Our multimodal AI found 1,426! The other 1,309 mentions lived only in the audio and visual frames.

The Workflow:
We built Oriane to act as the "eyes" for your AI stack. It indexes millions of videos at the frame level: detecting logos, spoken words, and product placements.

1. Search: Use Oriane to surface every untagged mention of your brand (or competitor).
2. Analyze: Export the raw data and use our Prompt Library (oriane.xyz/prompt-library) with your own LLM (Claude/GPT/whatever you prefer).

You get 80% more coverage at a fraction of the cost of legacy enterprise stacks.

For those running high-volume UGC, what’s your current strategy for tracking untagged content?