SaaS Metrics
Product Positioning & Context
91% of internet bandwidth is led by videos that your AI does NOT watch... Oriane watches millions of social videos a day, and turns what's on screen, in audio and in captions into a structured intelligence layer for teams and their AI apps to find niche creators, spot content trends, identify viral hooks and more!
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Oriane?
Oriane is a digital product or tool described as: The perception layer for Marketers and their AIs
Where did Oriane originate?
Data for Oriane was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Oriane publicly launched?
The initial public indexing or launch date for Oriane within our tracked developer communities was recorded on May 5, 2026.
How popular is Oriane?
Oriane has achieved measurable traction, logging over 146 traction score and facilitating 44 recorded discussions or engagements.
Which technical categories define Oriane?
Based on metadata extraction, Oriane is categorized under topics such as: Influencer marketing, Social media marketing, Pitch NYC.
What are some commercial alternatives to Oriane?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Qwen3.5-Omni, which offers overlapping value propositions.
Are there open-source alternatives related to Oriane?
Yes, the GitHub ecosystem contains correlated projects. For example, a repository named nikmcfly/MiroFish-Offline shares highly similar architectural descriptions and topics.
How does the creator describe Oriane?
The original author or development team describes the product as follows: "91% of internet bandwidth is led by videos that your AI does NOT watch... Oriane watches millions of social videos a day, and turns what's on screen, in audio and in captions into a structured inte..."
Community Voice & Feedback
The watches millions of video a day claim is wild .Would love to understand the infrastructure behind that.
Comparing this to tools like Brandwatch and Meltwater is smart they really do treat video like text.
Curious how deep the analysis goes are you just tagging content or actually understanding hooks, pacing, and storytelling patterns?
The Jellysmack creators ecosystem origin story makes this much more credible. You’ve clearly felt the pain firsthand.
congrats on the launch, oriane team! how did you cracked inference problem?
Truly love the product! As I’m getting started with IG, Oriane helped me to figure out what content to build and what works! Thanks for building this @julien_rosilio and @yuri_mihaileanu1 🙌🙌🙌
@julien_rosilio Watching this from the music side. The luxury wedge makes senseas a beachhead, but the perception layer applies to everything.We have the same "captions don't capture the content" problem,just with audio fingerprinting, sample tracking, and unauthorizedderivative use.Question for the team: if you were going to expand the perceptionlayer beyond brand listening, which adjacency would you pickfirst? Music IP detection? Sports broadcast monitoring? Liveevent surveillance? Curious how you're thinking about the secondbeachhead.
Habéis considerado un plan de pago por videos procesados?
Very interesting. On the technical side, how do you manage to have such low latency with so many videos and what I imagine is a vector search? You used pinecone or qdrant?
Just gave it a spin on the free version, looks pretty solid. How do you manage to vectorize so much videos without running out of money?
the 1000x cost reduction on vectorization is the actual unlock here. built a tiktok audio trend pipeline last year for a content channel and the math on doing it at scale across millions of videos was brutal at the time. brandwatch treating video like text has been the wedge sitting there for years, surprised it took this long for someone to go after it properly
"Captions and metadata aren't the content" should be on a t-shirt.That sentence is the entire wedge against legacy social listening. Every PMM working in brand intelligence is going to steal it within the month. Watch.
@julien_rosilio Congrats on your launch 🙌This feels like a natural evolution. Social listening went text → sentiment → image recognition → video intelligence. Each step was inevitable.
This sounds interesting for creator research. Which platforms are you pulling videos from right now, like TikTok and Reels or more YouTube Shorts?
Thibaut here, CTO 👋Quick note on the cost reduction Julien mentioned, happy to nerd out on the infrastructure side if anyone's curious. The short version: the whole industry approaches video understanding as "extract everything, then index", we flipped it. We took the cost to vectorize 100K videos from $60K to $60. That's 1,000x. It's why this category is suddenly viable.Engineers in the comments, fire away.
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
No mainstream media stories specifically mentioning this product name have been intercepted yet.
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.