Well, there are a lot of products being made and sold using Raspberry Pi. I have seen people use it to make interactive content on screens at escape rooms, and use it as a computer for digital signage that shows the same content on a loop.
6 months ago, I also started experimenting with Raspberry Pi, and now I am working on a Chromecast alternative for businesses called Soljacast using RPi. My device can work as digital signage, or a casting device for co-workings and event companies that use computers at events for presentations.
I am also using it to build a personal companion AI device with a screen, sort of like an Amazon Echo Show but with Microsoft's Clippy in there. So I am sure people are building even more complex stuff than me.
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Recently I gave Catnip a try and it works very smoothly. It works on web via GitHub workspaces and also has mobile app.
https://github.com/wandb/catnip
I agree with the second part in terms of differentiation you mentioned.
That plus the ability to provide customized solutions that stitch together data extraction and business logics such as reconciliations for vendor payments or sales.
I think both these reasons are what's keeping all the OCR based companies going.
My only advice would be to figure out more USPs before native models eat your lunch. Like Nanonets has its own native OCR model.
AI models will eventually do this natively. This is one of the ways for models to continue to get better, by doing better OCR and by doing better context extraction.
I am already seeing this trend in the recent releases of the native models (such as Opus 4.5, Gemini 3, and especially Gemini 3 flash).
It's only going to get better from here.
Another thing to note is, there are over 5 startups right now in YC portfolio doing the same thing and going after a similar/overlapping target market if I remember correctly.