Launched in 2015 and based in Tel Aviv, Syte provides visual-search and product-discovery tools for e‑commerce, with products including Visual Discovery, Searchendising (auto-tagging) and a Discovery Marketplace. Its visual AI claims more than 95% object-matching accuracy in under a second and has been trained on millions of products, enabling multi-object recognition and detailed tagging. Syte counts customers such as Farfetch, Fashion Nova, Castorama and Signet Jewelers and reports a 38% customer-base growth since early 2020. The company says its solutions boost conversion by 177% on average and deliver a 423% average increase in ROI for brands. Syte is planning geographic expansion into the U.S. and Asia‑Pacific and intends to develop or acquire product-discovery technology spanning visual, text, voice and other modalities to broaden personalization. Syte builds a camera-based visual search engine and product-discovery tools that let shoppers snap a picture and find matching or similar items on retailer sites. Its platform includes a recommendation engine, tagging services for catalogs and an API for retailer integration. The company has landed customers across fashion and general retail, including Farfetch, River Island, Argos, Sainsbury’s and Kohl’s. Syte launched a product about 18 months after roughly three years of development and says it has seen rapid adoption since then. It is expanding into the U.S. market and exploring physical-retail use cases such as smart mirrors and mobile apps for store assistants. Syte projects 300% revenue growth this year and has not disclosed a valuation. Syte.ai builds a deep-learning visual search engine focused exclusively on fashion products, enabling users to hover over items in photos and get results for similar products on sale. The company offers search tools for online publishers and retailers and also publishes consumer products including a Chrome extension called Fashion Lover and a chatbot named Glamix. Syte.ai monetizes by sharing revenue with publishers and charging e-commerce stores a subscription fee. Founders spent the prior three years developing the deep-learning algorithms, which the company describes as “building a bridge between physics and fashion” to improve how machines understand images. After integration, the search can surface alternative items when products are out of stock and increase visibility of indexed inventory. The company plans to use its Series A to drive marketing and growth in the U.S. and to pursue partnerships and deals in Asia with its new Asian backers.

Total raised

$70M

Funding rounds

4

Latest round

Series C

Latest activity

Oct 2020