Launching today
Viso Now
Build computer vision applications with AI
99 followers
Build computer vision applications with AI
99 followers
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.
Viso Now
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!
@taliambender really like that the review queue is optimized for speed. Ops teams will love this congrats for launchig🙌
Viso Now
@taliambender to me the most amazing part is that the computer vision projects that used to take 2, 3, 12 months can now be done in minutes! Some of our long term "traditional" computer vision clients are adding Viso Now to their ecosystem because it can reason and understand context much better than anything the market has seen before!
Viso Now
Making computer vision more accessible is exciting. Doing it while building a second product at our stage is the harder part.
That’s what makes Viso Now meaningful to me. It lowers the barrier for others to experiment with computer vision, while also representing a pretty significant step for us as a Seed-stage company.
Traditional computer vision often depends on specialist talent, complex infrastructure, and a lot of upfront investment just to get an idea off the ground. Those same constraints show up inside a startup too, with limited resources, constant prioritisation, and the challenge of deciding where every dollar of investment should go.
Working across finance and operations at viso.ai, I see those trade-offs first-hand. Proud of the team 🚀
Viso Now
Hi Product Hunt! I'm Gerard the CTO at viso.ai.
As CTO, this launch is especially meaningful to me because Viso Now changes something fundamental about how computer vision software gets built.
For years, the hard part of computer vision was the model. Turning a camera feed into a useful application meant model selection, dataset generation and model training pipelines all of which require time and technical experitse - often before you can validate whether the requirements of the use case can be sufficiently met.
The hard part now is everything around the model: routing observations into a workflow a human can actually act on, reviewing outcomes at speed, setting thresholds that mean somethings and visualising the results for the business.
With Viso Now, we wanted to invert the workflow: start with the problem you want to solve in the physical world, describe it, and let the system build the vision logic around it. You can connect cameras, iterate on the behavior, and get to a working application dramatically faster.
But this ins't all value truly lands when someone can act on it. Viso Now automatically turns that logic into live dashboards: detections become charts, trends, and metrics that speak the language of the business. And through connectors, relevant outputs are sent straight into the tools your team already use.
Under the hood, making something feel this simple required solving a lot of hard engineering problems around vision reasoning, video processing, orchestration, and turning ambiguous natural-language intent into reliable application logic.
That’s been the fun part for our engineering team!
We’re still early, and I’d love feedback from builders here - especially on the kinds of real-world problems you wish to solve and the challenges you face.
Happy to answer any questions about the product or where we’re taking it next. 🚀
Really interesting. Can I integrate cctv cameras to viso now? Getting data from the physical world sounds good
Viso Now
@fernando_sanchez11 Hi Fernando, thank you for the question. Yes, you're able to connect cameras directly to Viso Now. You will find a camera connector inside Settings > Connectors.
Can I connect viso now to other agents?? or how does the output integrations works? Looks great btw ❤️❤️
Viso Now
@juan_diego_lopez_guillen Hi Juan Diego! YES, You can connect Viso Now with other agents through our webhook and MQTT output connectors. Settings > Output Connectors.
What does it mean when it says 'building vision logic'? Is it combining pre-built models, or is it something else?
Btw, Congratulation's @taliambender & team @Viso Now . 🚀🎊
Viso Now
@aymi_malik Great question! Building vision logic in our app means that under the hood our agent is building the required logic for the vision models to detect the specific usecase, eg the app purpose, configuration etc.
Viso Now
Been an exciting journey building @Viso Now from the frontend side.
Seeing everything come together into a platform that makes computer vision more accessible and actually useful has been really rewarding.
A lot of thought, iteration, and teamwork has gone into this.
Really proud of what the team has built.