Twiggle releases API to extend AI capabilities to e-commerce sites
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Image Credit: Twiggle

The Alibaba-backed Israeli startup is changing how you experience shopping online with innovative search that speaks your language

Just under a year after their $12.5 million Series A funding round, Israeli Artificial Intelligence for e-commerce startup Twiggle announced today the release of their Semantic API product, bringing their accumulated expertise in search to the wider online shopping market.

Twiggle was co-founded in December of 2013 by CEO Dr. Amir Konigsberg, previously one of the members of Google’s emerging markets operations, and Dr. Adi Avidor, a former engineering tech lead at Google. In the time since their Series A, they have picked up another $5 million from Alibaba, doubled their team, and moved shop to new offices overlooking Tel Aviv.

Describing what they have built in short, this company has made search for e-commerce usable to the point that it becomes a near enjoyable experience. Their engine processes through lists of products, understanding the attributes that make them what they are. Instead of simply looking for a few random keywords drawn from a page, Twiggle can build out “family trees” of products, understanding what belongs in which categories.

While this sounds simple enough that it should be the industry standard, the reality generally falls flat of our expectations.

Take this example of looking for a “black dress without sleeves” on Amazon, which is generally considered to be one of the better sites out there.

Image Credit: Twiggle

Judging by the results that showed up on the page, we can assume that Amazon was just looking at the keywords black, dress, and sleeves. However it seems to have missed the importance of “without” and how it affects the search sentence. If you had gone into a brick and mortar store, hopefully the sales girl would understand what you mean when you say that you are looking for a “black dress without sleeves.”

So why should an e-commerce site be any different?

Image Credit: Twiggle

What they have figured out is how to dissect a query and understand not just how the individual words match to products in their directory, but what is the context of the words put together in human speak.

Twiggle is able to translate the product into terms like black being a color, that a dress is the noun being described, and that the sleeves is an attribute. This is important because we should want technology to better adapt itself to us, learning how we communicate. Not the other way around.

Meeting at their office for the demo, Konigsberg told Geektime that they have succeeded in scaling this process up to hyper speed, successfully being able to perform this learning process about new products at a rate of over 100 million products in less than an hour.

Why is speed so important here? Well, in order to keep up with the constant changes in inventory that are posted to an e-commerce website, being quick on the draw is key in getting the product properly labeled so that it can be found by shoppers.

“A product has to be searchable before it is actually sellable,” Twiggle’s VP Product Noa Ganot says, who spent a number of years over at eBay and has a pretty good handle on how people think about searching for items to buy online.

E-commerce and beyond

Funded by big players like Alibaba and Naspers, Twiggle seems to have figured out the “how” for making products truly searchable on a large scale. What they have yet to decipher is, based on who has invested in them so far, how they expand outside of the major e-commerce players, some of whom we can assume they are working with as current customers?

Simply put, how do they democratize AI, bringing the capabilities to the rest of the retail market?

This is where Semantic API comes in, sitting atop a client’s own search and rapidly integrating at no risk to the user.

“We specifically designed our technology to enhance our customers’ existing search, not replace it,” explains VP Marketing Yael Citro. “Our customers stay in control and decide how to balance Twiggle’s signal with all of their other signals. This, plus the fact that it can be up and running in a matter of weeks — gives us a huge competitive advantage.”

Amplifying their advantage is that when new clients start using the API, they receive all of the data and experience that Twiggle has gained over the past few years of working on their product.

“We have a full data infrastructure that learns from whatever we can get our hands on,” Konigsberg comments. “Learning from partners and mining the web to create an e-commerce repository that has a representation of the e-commerce world. If anything new comes up in e-commerce, not related to a specific customer.”

They can draw their data from queries on brand websites, marketplaces, user behavior, product info and plenty of other sources, learning more as they go.

“We’re counting on them wanting to focus on what they do best, which is to run their business,” adds Konigsberg, describing how they can more extensively improve the search for their clients, a task that most retailers would be unable to take on by themselves. “AI is not something that you can do while having a different business. It’s something that requires a lot of talent and effort.”

To Twiggle, search is not just a visitor looking for an item. Instead it is a core principle of the experience that they believe generates conversions for the seller. If a visitor is frustrated with the search and cannot find the products that they want, then they are unlikely to buy from that brand now or in the future.

If you are Amazon, then this may not be quite as much of an issue. However if you are a smaller seller, then you have don’t have a choice but to improve this feature.

“People’s expectations are rising from e-commerce since Facebook and Google are giving them better search results,” posits Konigsberg. “They are going to demand better based on the entirety of their experience.”

Along with their API, Twiggle will be coming out with an analytic product. They tell Geektime that this will go out first for their clients to help identify which opportunities are being missed, and evaluate how well Twiggle is performing for them.

Photo credit: Twiggle

Twiggle co-founders Dr. Adi Avidor and Dr. Amir Konigsberg Photo credit: Twiggle

Machine learning evolves

Over the past year, Israel has sprouted fields of companies purporting to leverage AI, showing up in everything from schedulers to insurtech products that find you the right policy. By overusing the term, it feels like they have cheapened the real work that is going on in AI, making it so noisy that it can be hard to tell the difference between someone with a decent algorithm and another who has really delved into the tech and come up with something incredible.

At this point, there are a lot of folks doing some pretty decent work with Machine Learning and other forms of pattern recognition that are capable of some pretty cool tricks. But who is actually teaching computers the real learning capacity that defines AI?

Take Zebra Medical Vision and Nexar as examples, pushing such technology towards the breakthroughs it will need to actually become useful. What separates Twiggle from other machine learning startups and in my mind elevates them is their approach to it by applying it to relevant, operable business needs. Twiggle is constantly learning, taking new input to become naturally smarter.

During my demo with their search, I threw in a query for “sunglasses for tweens” to see if I could trick it up. Being fairly certain that “tweens” was not present in the first edition of Webster’s, their system did not recognize the term and just showed me a great selection of sunglasses.

The fact Twiggle’s engine could not identify a ‘tween’ was fine, because I know that the system will recognize it next time. Over time as the system continues to devour more information to understand how we speak and think about products, they will continue to build added value for new customers who can benefit from this wealth of experience. Their understanding of human language with advanced natural language processing (NLP) is helping to ensure this.

Can the technology, capable of high-level processing and data crunching, make real human beings feel understood and better served?

In the case of e-commerce, the shopper needs to feel understood if they are going to buy. Twiggle is clearly leading in this field, establishing themselves as a cornerstone in the industry.

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Gabriel Avner

About Gabriel Avner

Gabriel has an unhealthy obsession with new messaging apps, social media and pretty much anything coming out of Apple. An experienced security and conflict consultant, he has written for The Diplomatic Club, the Marine War College, and covers military affairs with TLV1 radio. He mostly enjoys reading articles wherever his ADD leads him to and training Brazilian Jiu Jitsu. EEED 44D4 B8F4 24BE F77E 2DEA 0243 CBD1 3F7C F4B6

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