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This is really cool! I have a few questions 1. Is there really no other competitor or company that tried to tackle this problem in the past? It feels like a r

This is really cool! I have a few questions 1. Is there really no other competitor or company that tried to tackle this problem in the past? It feels like a really common usecase and someone must've done something about it! 2. Do you have a fixed set of words that the user should use to query? I'm an AI researcher/practitioner who worked in this area. It's super difficult to search for tail objects in images/text. 3. Why API first?

mvoodarla 5y ago
Thanks for the thoughtful questions! 1. People have definitely tried this in the past in terms of having similar tech, but I don't think anyone has sanely approached it at the same angle. We're seeing so many more companies like EquipmentShare (https://www.equipmentshare.com/ https://www.equipmentshare.com/), UpKeep (https://www.upkeep.com/ https://www.upkeep.com/), Spot AI (https://www.spot.ai/ https://www.spot.ai/) and others which are building core software for industries without necessarily having tons of video expertise in-house. Also other video-first companies like Gong (https://www.gong.io/ https://www.gong.io/), Mux (https://mux.com/ https://mux.com/), and Loom (https://www.loom.com/ https://www.loom.com/) are building platforms that might benefit from visual analysis at scale. Things like serverless functions were also less cool before. 2. There's a bunch of ways to search using Sieve. Some are fixed variables, others are visual similarity based. See here: https://sievedata.com/faq https://sievedata.com/faq 3. Our target is the full stack developer within companies building cloud-first software for specific industries. Easy-to-use APIs are key because of this. They are the ones with the expertise to package it into a product that really works for the end user, and we're the ones with computer-vision and video expertise.
mandeepj 5y ago
> Is there really no other competitor or company that tried to tackle this problem in the past? Microsoft and Google already have very mature offerings in this space. https://azure.microsoft.com/en-us/services/media-services/#getting-started https://azure.microsoft.com/en-us/services/media-services/#g...
- mvoodarla 5y ago
  It's a good point. While Microsoft and Google offer video processing / analysis, none of them are API-first in the same way we are. 1. We process videos, and can store their metadata / embeddings in perpetuity. This means a simple query API, don't worry about storing a DB of metadata yourself. 2. "Reverse Image Search" - Say a user cares about something specific, like a blue box that's toppled over. Using high-dimensional representations of certain frames in video, we can surface every time this scenario shows up in the future, based on a user having marked it as "interesting" in the past. 3. Our "killer" app is the video processing infra that's super modular. We give this flexibility to the full-stack dev to plug in their own models, or use ours. A final thing to note is that we're going after a completely different set of companies, specifically companies focused on building software for a specific industry (i.e. https://www.equipmentshare.com/ https://www.equipmentshare.com/) that eventually want to add video analytics as an offering. It's also unclear who Azure or GCP are targeting though it seems like it's for general media content.
  - mandeepj 5y ago
    Thanks for the detailed response. I wish you all the luck in your journey. > 2. "Reverse Image Search" - Say a user cares about something specific, like a blue box that's toppled over. Using high-dimensional representations of certain frames in video, we can surface every time this scenario shows up in the future, based on a user having marked it as "interesting" in the past. I believe a lot of retail/consumer companies would be interested just in that. Example use case - restaurants trying to find repeat customers or gauging their mood before/after the meal.

mvoodarla 5y ago

Thanks for the thoughtful questions! 1. People have definitely tried this in the past in terms of having similar tech, but I don't think anyone has sanely approached it at the same angle. We're seeing so many more companies like EquipmentShare (https://www.equipmentshare.com/ https://www.equipmentshare.com/), UpKeep (https://www.upkeep.com/ https://www.upkeep.com/), Spot AI (https://www.spot.ai/ https://www.spot.ai/) and others which are building core software for industries without necessarily having tons of video expertise in-house. Also other video-first companies like Gong (https://www.gong.io/ https://www.gong.io/), Mux (https://mux.com/ https://mux.com/), and Loom (https://www.loom.com/ https://www.loom.com/) are building platforms that might benefit from visual analysis at scale. Things like serverless functions were also less cool before. 2. There's a bunch of ways to search using Sieve. Some are fixed variables, others are visual similarity based. See here: https://sievedata.com/faq https://sievedata.com/faq 3. Our target is the full stack developer within companies building cloud-first software for specific industries. Easy-to-use APIs are key because of this. They are the ones with the expertise to package it into a product that really works for the end user, and we're the ones with computer-vision and video expertise.

mandeepj 5y ago

> Is there really no other competitor or company that tried to tackle this problem in the past? Microsoft and Google already have very mature offerings in this space. https://azure.microsoft.com/en-us/services/media-services/#getting-started https://azure.microsoft.com/en-us/services/media-services/#g...

mvoodarla 5y ago
It's a good point. While Microsoft and Google offer video processing / analysis, none of them are API-first in the same way we are. 1. We process videos, and can store their metadata / embeddings in perpetuity. This means a simple query API, don't worry about storing a DB of metadata yourself. 2. "Reverse Image Search" - Say a user cares about something specific, like a blue box that's toppled over. Using high-dimensional representations of certain frames in video, we can surface every time this scenario shows up in the future, based on a user having marked it as "interesting" in the past. 3. Our "killer" app is the video processing infra that's super modular. We give this flexibility to the full-stack dev to plug in their own models, or use ours. A final thing to note is that we're going after a completely different set of companies, specifically companies focused on building software for a specific industry (i.e. https://www.equipmentshare.com/ https://www.equipmentshare.com/) that eventually want to add video analytics as an offering. It's also unclear who Azure or GCP are targeting though it seems like it's for general media content.
- mandeepj 5y ago
  Thanks for the detailed response. I wish you all the luck in your journey. > 2. "Reverse Image Search" - Say a user cares about something specific, like a blue box that's toppled over. Using high-dimensional representations of certain frames in video, we can surface every time this scenario shows up in the future, based on a user having marked it as "interesting" in the past. I believe a lot of retail/consumer companies would be interested just in that. Example use case - restaurants trying to find repeat customers or gauging their mood before/after the meal.