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AI Startup Anglera, supported by Y Combinator, launches with a focus on enhancing product data using artificial intelligence technology.

AI Developments Face a Hidden Infrastructure Crisis: According to Anglera, as more AI applications are being created, there's a significant underlying infrastructure shortage, leading to breakdowns at scale.

Artificial intelligence startup Anglera, funded by Y Combinator, launches with a focus on enhancing...
Artificial intelligence startup Anglera, funded by Y Combinator, launches with a focus on enhancing product data through AI solutions.

AI Startup Anglera, supported by Y Combinator, launches with a focus on enhancing product data using artificial intelligence technology.

In the rapidly evolving world of e-commerce, managing product data efficiently has become a critical challenge for retailers and marketplaces. Enter Anglera, a groundbreaking startup that has launched an AI product data enrichment technology, aiming to streamline product data management and revolutionise the e-commerce landscape.

Founded by Stanford AI graduates Amay Aggarwal and Ray Iyer, who previously worked at Uber Eats, Anglera has set out to bridge the massive infrastructure gap beneath AI apps that has been causing issues such as poor discoverability, low conversion rates, and delays in getting new products live due to manual data entry.

Anglera's technology transforms messy product data, including spreadsheets, PDFs, brand websites, and supplier feeds, into structured, searchable, and enriched content at scale. This AI-driven solution automatically cleans, structures, and enriches product data, reducing manual effort and improving data quality.

One of the key advantages of Anglera's technology is its scalability. The system supports enrichment workflows that power millions of SKUs across over 20 major retailers and marketplaces, enabling businesses to handle large and growing catalogs seamlessly.

The benefits of using Anglera's technology are substantial. E-commerce companies can prioritise business growth strategies rather than spending resources on fixing broken product data infrastructure. Customers see immediate lifts in time-to-market, SEO, and conversion after enriching their product content.

Arbor CEO Farshad Taheri attests to the impact of Anglera's technology, stating that adding new products to their store used to take weeks, but now they can go live with new products within the same day. Adam Froeser, the co-founder of SecondShop, shares similar sentiments, stating that before Anglera, his team spent 20 minutes manually preparing each product for sale, but now it's down to one click of a button.

SKUs get processed in a matter of seconds, rather than weeks, thanks to Anglera's AI agents that ingest and process diverse product data sources, converting them into standardized, AI-ready product listings. This eliminates the labor-intensive task of manual data cleaning and structuring.

Anglera's vision is a world where every product is instantly discoverable by AI agents, product information flows seamlessly between systems, and new products go to market in hours, not months. With the rise of agentic commerce, where AI agents are starting to make purchases autonomously, this vision could not be more timely.

As AI models like ChatGPT, Perplexity, and others become primary product discovery channels, the need for efficient and accurate product data management becomes even more crucial. Anglera's AI product data enrichment technology is poised to play a significant role in shaping the future of e-commerce.

[1] Anglera. (n.d.). Retrieved from https://www.anglera.ai/ [3] Anglera. (2021, March 22). Retrieved from https://techcrunch.com/2021/03/22/anglera-raises-10-5-million-for-ai-powered-product-data-enrichment/

Artificial Intelligence, integrated in Anglera's technology, automates the process of cleaning, structuring, and enriching product data, aiming to improve data quality and reduce manual effort. This innovation, developed by Stanford AI graduates, enables e-commerce companies to manage product data more efficiently, ultimately prioritizing business growth strategies over fixing broken data infrastructure.

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