Best guess is that there's some level of semantic analysis going on beyond just OCR, which tips the balance (at least for me) to being an "AI" feature based on loose current usage of that term.
The clue is that in this demo https://www.youtube.com/watch?v=aZbHd4suAnQ&t=1062s they show searching for "blue pantsuit with sequin lace" finds something that was described as "peacock" in the text. It looks like this is based on embedding search against image embeddings.
And... one of the 3 SQLite databases created by Recall is called "SemanticImageStore" - which suggests to me there may be a CLIP-style image embeddings model being run on-device here.
Those databases contain "diskann" columns which are likely a reference to Microsoft's vector indexing library: https://github.com/microsoft/DiskANN
Best guess is that there's some level of semantic analysis going on beyond just OCR, which tips the balance (at least for me) to being an "AI" feature based on loose current usage of that term.
The clue is that in this demo https://www.youtube.com/watch?v=aZbHd4suAnQ&t=1062s they show searching for "blue pantsuit with sequin lace" finds something that was described as "peacock" in the text. It looks like this is based on embedding search against image embeddings.
And... one of the 3 SQLite databases created by Recall is called "SemanticImageStore" - which suggests to me there may be a CLIP-style image embeddings model being run on-device here.
Those databases contain "diskann" columns which are likely a reference to Microsoft's vector indexing library: https://github.com/microsoft/DiskANN