Viso Now is a self-building AI vision platform that turns images, video, and camera feeds into working computer vision applications. Describe what you want to understand, and Viso Now builds the agentic vision logic and custom live dashboards. All without model training, annotation, or writing code.
Hey everyone 👋 I'm Talia, member of the viso.ai team!
So, why Viso Now. As we've been in the computer vision space for quite some time, we saw that our customers kept running into the same annoying gap on their projects: the model does its job and flags something, but then what? In practice, that "then what" turns into someone hacking together a script to dump detections somewhere, a human squinting at a spreadsheet, and alerts that either don't exist or exist in someone's inbox filter that nobody trusts. We got tired of rebuilding that glue every time, so we built it once, properly.
What that looks like in practice: you point Viso Now at your footage or images, it routes anything flagged into a review queue that's actually built for a person to move through quickly (not a data scientist, an actual ops or QA person), and it pings you the moment something crosses whatever threshold matters to you.
The part I like most is that it doesn't care what your "something" is. We've had early users piloting it for things as different as catching defects on a production line and, genuinely one of my favorite examples, a small marine biology team using it to track coral bleaching across reef survey footage. Same review-and-alert loop underneath, wildly different Tuesday for the person using it.
If you're running or experimenting with a CV model and have hit that same "okay but who actually looks at this" wall, I'd love to hear about it. Drop a comment, and I'll be in touch today!
About Viso Now on Product Hunt
“Build computer vision applications with AI”
Viso Now launched on Product Hunt on September 10th, 2026 and earned 103 upvotes and 21 comments, placing #12 on the daily leaderboard. Viso Now is a self-building AI vision platform that turns images, video, and camera feeds into working computer vision applications. Describe what you want to understand, and Viso Now builds the agentic vision logic and custom live dashboards. All without model training, annotation, or writing code.
On the analytics side, Viso Now competes within Artificial Intelligence, No-Code and Vibe coding — topics that collectively have 485.6k followers on Product Hunt. The dashboard above tracks how Viso Now performed against the three products that launched closest to it on the same day.
Who hunted Viso Now?
Viso Now was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Viso Now including community comment highlights and product details, visit the product overview.
Ben Lang
Hey everyone 👋 I'm Talia, member of the viso.ai team!
So, why Viso Now. As we've been in the computer vision space for quite some time, we saw that our customers kept running into the same annoying gap on their projects: the model does its job and flags something, but then what? In practice, that "then what" turns into someone hacking together a script to dump detections somewhere, a human squinting at a spreadsheet, and alerts that either don't exist or exist in someone's inbox filter that nobody trusts. We got tired of rebuilding that glue every time, so we built it once, properly.
What that looks like in practice: you point Viso Now at your footage or images, it routes anything flagged into a review queue that's actually built for a person to move through quickly (not a data scientist, an actual ops or QA person), and it pings you the moment something crosses whatever threshold matters to you.
The part I like most is that it doesn't care what your "something" is. We've had early users piloting it for things as different as catching defects on a production line and, genuinely one of my favorite examples, a small marine biology team using it to track coral bleaching across reef survey footage. Same review-and-alert loop underneath, wildly different Tuesday for the person using it.
If you're running or experimenting with a CV model and have hit that same "okay but who actually looks at this" wall, I'd love to hear about it. Drop a comment, and I'll be in touch today!