Whatnot acquires Shaped to power real-time live shopping recommendations
Livestream shopping platform Whatnot has acquired AI startup Shaped, a machine learning company focused on real-time recommendations and search. The deal will bolster Whatnot’s personalization and discovery features as it expands into new product categories.
Livestream shopping app Whatnot announced Wednesday that it has acquired Shaped , a machine learning company that specializes in real-time recommendation and search systems. The deal is meant to strengthen Whatnot’s discovery and personalization capabilities as the platform continues to expand across new product categories and millions of buyers.
According to the company, the acquisition helps Whatnot continue its investment in AI as it looks to solve one of live commerce’s biggest challenges: helping shoppers find the right products while inventory, auctions, and buyer demand change in real time.
Unlike traditional e-commerce platforms, where product catalogs remain relatively stable, Whatnot’s marketplace is constantly evolving, and live auctions can end within minutes or last for hours.
“By combining Shaped’s technology with Whatnot’s existing systems, we can make recommendations faster, more responsive, and more personalized,” Emmanuel Fuentes, VP of Data and AI at Whatnot, told TechCrunch. “That speed matters because live commerce is a uniquely hard recommendation problem. Inventory changes by the second, shows start and end continuously, and buyer intent shifts throughout a show.”
Fuentes said the company has spent the last six years improving the speed of its recommendation engine, reducing recommendation latency from roughly a day to just minutes. Integrating Shaped’s technology is expected to push those recommendations even closer to real time. The company says its systems process more than 500,000 hours of live video and millions of real-time interactions every week, using that data to continuously improve recommendations.
Founded to help businesses build AI-powered recommendation systems, Shaped developed technology that combines existing customer data with large language models and machine learning to deliver highly personalized search and discovery experiences. Its customer roster included companies such as Outdoorsy and QVC.
As part of the acquisition, Shaped founder and CEO Tullie Murrell, along with nearly a dozen engineers and AI researchers, will join Whatnot. Murrell will lead the company’s newly formed Applied AI Research group. (Notably, Murrell worked at Meta before launching Shaped.)
The acquisition comes as Whatnot experiences significant growth. Launched in 2019, the company recently revealed that sellers have surpassed 1 billion orders. Last year, Whatnot raised $225 million in Series F funding, giving the company a valuation of more than $11 billion after adding 20 million buyers over the past year.
Whatnot has also significantly broadened its marketplace, launching more than 35 new categories last year — including art, golf, and vinyl — and more than 45 additional categories during the first half of 2026, with new subcategories continuing to roll out each month.
Additionally, the move comes as resale giants race to integrate AI throughout their platforms, such as eBay and Poshmark .
This story has been updated to correct dates that incorrectly referred to the first half of 2025. The correct time period is the first half of 2026
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Pontos-chave
- A aquisição da Shaped pela Whatnot destaca a importância da personalização em plataformas de e-commerce.
- A integração de tecnologias de recomendação em tempo real pode inspirar inovações em startups brasileiras.
- O comportamento do consumidor está mudando, exigindo experiências de compra mais dinâmicas e personalizadas.
Análise editorial
A aquisição da startup Shaped pela Whatnot é um movimento estratégico que destaca a crescente importância da personalização em plataformas de e-commerce, especialmente no contexto do live commerce. No Brasil, onde o comércio eletrônico tem visto um crescimento exponencial, a implementação de tecnologias de recomendação em tempo real pode ser um diferencial competitivo significativo. As empresas que investem em IA para melhorar a experiência do usuário estão se posicionando para capturar uma fatia maior do mercado, que se torna cada vez mais exigente em relação à personalização e relevância das ofertas.
Além disso, a integração da tecnologia da Shaped pode inspirar startups brasileiras a desenvolverem soluções semelhantes, focadas em inteligência artificial e aprendizado de máquina. O Brasil possui um ecossistema de startups vibrante, e a crescente demanda por soluções de e-commerce mais inteligentes pode estimular inovações locais. O sucesso da Whatnot pode servir como um case de referência para empresas brasileiras que buscam adotar práticas semelhantes em suas operações.
Outro ponto a ser observado é a evolução do comportamento do consumidor. À medida que os compradores se acostumam com experiências de compra mais dinâmicas e personalizadas, a pressão sobre as plataformas de e-commerce para se adaptarem a essas expectativas aumentará. O desafio será não apenas implementar a tecnologia, mas também garantir que a experiência do usuário seja intuitiva e eficaz, algo que a Whatnot parece estar priorizando com essa aquisição.
Por fim, a movimentação da Whatnot pode sinalizar uma tendência mais ampla no setor de e-commerce, onde a fusão de tecnologia e comércio ao vivo se torna cada vez mais prevalente. Com o crescimento das transmissões ao vivo e a interação em tempo real, as empresas que não se adaptarem a essa nova realidade podem ficar para trás. Portanto, é crucial que as empresas brasileiras observem essas tendências globais e considerem como podem incorporá-las em suas estratégias de negócios para se manterem competitivas no mercado.
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Fonte original:
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Este artigo foi curado e publicado pelo AIDaily como parte da nossa cobertura editorial sobre desenvolvimentos em inteligência artificial. O conteúdo é baseado na fonte original citada abaixo, enriquecido com contexto e análise editorial. Ferramentas automatizadas podem auxiliar tradução e estruturação inicial, mas a decisão de publicar, a revisão factual e o enquadramento de contexto seguem responsabilidade editorial.
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