https://open-gpt.app/ VS Ultranivo

Ultranivo's answer:

It started as a tool built for personal use. One channel I follow has hundreds of videos, plenty of them over an hour long, and the answer I wanted sat somewhere around minute 43 of a video whose title and description never mention it. A title gets indexed; what's said at minute 43 doesn't. The same problem shows up with a site holding a thousand PDFs — a tool can find the page listing the files, but it can't read across them, and the scanned ones have no text to index at all.

So I built something that pointed at the videos and documents I trusted and answered with the exact timestamp or the exact page. Friends used it more than expected. The realisation came later and was obvious in hindsight: that gap isn't personal, it's structural, and anyone sitting on a body of content has it pointed at their own audience. Ultranivo is that tool built properly — multi-tenant, branded, and made for someone else's audience rather than just my own.

Ultranivo's answer:

Ultranivo treats video as a first-class source, not an afterthought. Import a YouTube channel or upload video files and it transcribes, indexes and answers from what was actually said — citing the exact moment, so the viewer lands on the answer instead of scrubbing a timeline. Documents work the same way, down to the exact page, including scanned files that have no text layer.

The result is a complete assistant rather than a chat widget: your name, your logo, your colors, on your own domain or embedded on your existing site. Each customer gets their own database and vector collections, and you can charge your audience for access if you want to. No code and no developer at any point.

Ultranivo's answer:

Three things, honestly stated. Video is handled properly — transcribed, indexed and cited to the timestamp, not just documents with video bolted on. Answers are confined to your content and always attributable, so if the answer isn't in your material it says so rather than filling the gap. And it's a white-label product end to end: your branding, your domain, your own isolated data, with optional paid access built in rather than requiring a separate billing setup.

It's also worth being clear about what it isn't. It's a paid product with no free tier, and voice interface and web-search fallback aren't built yet.

Ultranivo's answer:

Businesses, non-profits, academies, creators — anyone with knowledge or information to share, or earn from. In practice that means anywhere the answers already exist but are too scattered or too long to find: a support library nobody reads, years of recorded lectures, a documentation archive, a back catalogue of long videos.

Buyers are typically non-technical, which is why setup requires no code and no developer.

Ultranivo's answer:

Next.js 16 and React 19 on the front end, Tailwind CSS. NestJS 11 on the API, MongoDB with Mongoose. Qdrant for vector search, with BAAI/bge-m3 embeddings and a reranking step before generation. Content ingestion runs as separate Python services: an OCR cascade for scanned documents, and captions or speech-to-text for video. Redis for rate limiting, Paddle as merchant of record.