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Putting The AI In Product Details: The Okkular Story | #196

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EP 196
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Nathan Bush is a director at eCommerce talent agency, eSuite. He has led eCommerce for businesses with revenue $100m+ and has been recognised as one of Australia’s Top 50 People in eCommerce four years in a row. You can contact Nathan on LinkedIn, Twitter or via email.

Abhi is the CEO and Co-founder of Okkular. He was previously a principal at Deloitte working with technology & media companies. He did his undergrad in Auckland before moving to Sydney.

The robots are coming to write your product descriptions!

In this episode of Add To Cart, we are joined by a guest who is looking to automate product tagging and descriptions so you never have to do them again. 

Abhishek Vohra is the CEO and Founder of the AI platform Okkular. Founded in New Zealand in 2019, Okkular helps retailers by taking images of products and automatically generating product tags that can be used in categorisation, filters and sorting, SEO and personalisation. They are even working towards creating automated product descriptions. Already , they have leading retailers including Blue Bungalow, Myer and Princess Polly using their AI solution.

“The need for data will only expand.  How you then use and leverage that data will become quite critical

Abhishek Vohra

Oil for the engine

“There’s a lot of talk about data is the new oil, right? So everyone refers to that.  So if I use the same analogy, then we are the technology that actually extracts and generates this oil, which can then be provided to other engines, which can then create better product discovery and better personalization, et cetera. 

And so there are lots of sort of engines out there which require good data. And so we are the company that actually generates this underlying data that can be fed into these engines.”

Marketplace to tech solution

So we’re basically four founders and three of us were flatmates in New Zealand when we were starting together doing our undergrad. And one went off and did his PhD in computer science, one moved to Melbourne and I moved to Sydney and we met another founder who was based in Melbourne.

So we were running this marketplace and we were uploading the products ourselves and making sure that we have tagged it, attributed it. And at some stage we said, look, man, this is just painful! I mean, we’re getting all these products, but we need to make sure that the end customers can find it and it was just taking up so much time.  So we sort of felt the pain ourselves in a way we were the e-comm coordinators collectively. 

And so this flatmate of mine at that time, he said, “Look, I’ll build you guys perhaps something which could help in automating this process.” So we started exploring that. It effectively started off as an internal tool to solve our problem.  And as we went through it, we realized, hang on, this is a better problem to be fixing. So we pivoted the business, we shut off the marketplace and we said, look, this is what I think we should go after.”

Train the brain 

“The best way that I can explain is think of it like it’s an artificial brain, right? So we have sort of curated a data set which would be filled with v-neck, square neck boat neck, etcetera.  So we are basically teaching this brain, okay, this is what v-neck is, square neck. And we sort of partnered up with a couple of fashion schools as well to build a taxonomy of what is relevant to fashion, what would be appropriate. 

And based on that, we then created data sets. Then we taught this artificial brain and then it starts sort of spitting out the output. And as it spits out the output, if it is incorrect, that’s where we getting others to audit that information and say, no, this is not quite right. And that becomes the feedback loop that goes back into it.”

“There’s a lot of talk about data is the new oil, right? So everyone refers to that.  So if I use the same analogy, then we are the technology that actually extracts and generates this oil, which can then be provided to other engines, which can then create better product discovery and better personalization, et cetera. 

And so there are lots of sort of engines out there which require good data. And so we are the company that actually generates this underlying data that can be fed into these engines.”

Questions answered in this episode include
  • In terms of product enrichment, what are the biggest gaps that you usually see with retailers? 
  • What’s the piece of product data automation that you haven’t been able to crack yet but would love to? 
  • What do you think the next frontier is for machine learning in retail? 

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