Dyver.AI

Octavian Dumitrescu recently joined ZF IT Generation (Ziarul Financiar) to discuss the launch of Dyver’s AI Assistant, marking an important milestone in the development of our product. Dyver.ai is now designed to support approximately 12 million small online stores worldwide, bringing together the e-commerce expertise of our team and collaborators with the power of today’s AI technology. A big thank-you to everyone who contributed to bringing this product to life! Link of the article (RO) is in the comments ↓ #Dyver #AIAssistant #Ecommerce #ProductData #CatalogManagement

New data format? IT ticket. New marketplace requirements? IT ticket. New supplier catalog? Weeks of manual cleanup. Sound familiar? For most catalog teams, every enrichment workflow depends on a developer, and every dependency means waiting. Meanwhile, products stay unenriched, and revenue waits with them. We built the Dyver.AI Assistant to remove that dependency completely. 1. Just describe what you need, in plain language. 2. The Assistant analyzes your input and output data. 3. It asks the right questions to understand your requirements. 4. It designs the enrichment workflow for you. 5. It explains every step, builds the workflow, tests it, and refines it based on your feedback. No manual work. No IT tickets. No waiting. What used to take weeks now takes minutes, and the catalog manager who knows the data finally owns the workflow. See it in action: the link is in the 1st comment. #Dyver #AIAssistant #Ecommerce #ProductData #CatalogManagement

It is live! Today, we are introducing Dyver AI Assistant, your personal product data enrichment expert. This is one of the things Dyver.ai set out to build from day one. A way for catalog owners to onboard a new supplier or a new marketplace autonomously. You describe what you need in plain language, at your own pace. Dyver AI Assistant asks the right questions to understand your requirements, builds the workflow, tests it, and refines it based on your feedback. You validate at any point. From messy to ready-to-publish data, through a conversation, for anyone dealing with products online. Register to use it on your own data! Link in the comments. #Dyver #Ecommerce #ProductData #CatalogManagement #Automation

Messy data. Impossible deadlines. Endless manual work. 👀 Every catalog team knows the drill. New products come in looking nothing like what your website or marketplace expects, and someone has to make sense of it, field by field, listing by listing. At Dyver.ai, we have been building a better way. One that turns that mess into something ready to publish, faster than you thought possible. We are launching it on Monday 🚀 #Dyver #Ecommerce #ProductData #CatalogManagement #Automation

RETAILORS had 47,000 Nike products sitting in physical stores across Scandinavia. No feed, no listings, no way to reach a single customer online. Miinto saw what others had ignored. Offline retail chains were an untapped partner segment. The potential was there. What was missing was the infrastructure to connect physical inventory to a live product feed. Dyver.ai built it. 47K products enriched and live. Stock and pricing are synced every 30 minutes across every store location. Zero manual input in the pipeline. Production-ready in weeks. Miinto had started building its own internal solution to solve this. After seeing what Dyver delivered, they moved fully to Dyver as their paid solution instead. The pipeline became a replicable model. Miinto can now onboard any offline retailer as a marketplace partner. A whole business segment that was previously out of reach. Swipe to see how it works. Full case study in the comments. #Ecommerce #ProductDataAutomation #Marketplace #FashionTech #Dyver

Shopify just confirmed it with a number: clean product data converts at 2x the rate of scraped data in AI-powered shopping. That is not a small edge. That is the difference between showing up and not showing up. Here is what changed: AI agents no longer just answer questions. They recommend products, add items to carts, and complete purchases. ChatGPT, Microsoft Copilot, Google AI Mode, and the Gemini app are now shopping interfaces, and what they surface depends entirely on how structured your catalog data is. If your product titles are vague, your attributes are missing, or your categories are inconsistent, the agent skips your product. Not because the channel does not work. Because your data does not. And this is no longer a Shopify-only problem. Amazon, Meta, Google, Microsoft, Stripe, Walmart, and Target have all backed the Universal Commerce Protocol, the open standard for how AI agents transact with merchants. The same catalog quality bar applies whether you run on Shopify, Magento, WooCommerce, or a custom platform. For a 50-SKU catalog, this is fixable manually. For a 1,000+ SKU catalog, it is not. We wrote on Dyver.ai about what this actually means operationally for large catalog operators. Link in comments. #Dyver #AI #AgenticCommerce #Shopify #DataQuality

70% of shoppers abandon cart before paying. Most e-commerce teams blame price. The real problem is friction, and it lives inside your checkout flow. We put together 6 of the most common checkout mistakes that quietly kill conversions: forced account creation, hidden costs, no trust signals at payment, visible empty discount field, no confirmation email, too few payment options. And there are more! You can learn more about checkout optimization by reading this article: https://lnkd.in/ecSgvaDD #Ecommerce #CartAbandonment #ConversionRate #Dyver #AI

The shopper is ready to buy. They just need a little help getting there. They opened your catalog looking for a city e-bike. 200 results, 40 filters, specs that read like a technical manual. They spent 20 minutes trying to figure out what any of it means for their actual commute. Not because the right bike was not there. It probably was. But finding it on their own was too hard, and their confidence never got high enough to push them to checkout. That is the discovery gap. And the good news is it is entirely fixable. Shoppers know their goals. They know their commute is 15km. They know there is a hill near the office. They do not know what motor torque means, and they should not have to. Ask them about their goals. Let the AI infer the specs. Get them to the right product with confidence. That is what AI-guided selling does. Swipe to see how it works. #Dyver #AI #ProductDiscovery #ConversionOptimization

Manual catalog work is easy to underestimate. At 100 SKUs, manual fixes are manageable. At 10,000 SKUs, they become the reason launches slow down, marketplace expansion gets harder, and product data quality starts to break. Our new article breaks down 8 ways AI helps prepare e-commerce catalogs for scale: from product descriptions and attribute enrichment to localization, deduplication, merchandising, and channel-specific content. The takeaway: AI does not eliminate catalog work. It changes who does it, how often it happens, and at what scale. The article is in the first comment. #Dyver #AI #Ecommerce #Catalog

Missed Octavian Dumitrescu on stage at Balkan eCommerce Summit? Here is the recap of his session: "We Have an AI Spacecraft. Why Is Cross-Border E-commerce Still Run on Spreadsheets?" We live in a world where AI can categorize, enrich, translate, and publish product listings in seconds. Yet many cross-border e-commerce teams still rely on spreadsheets to manage product data across marketplaces. In this session, Octavian explores why product data remains one of the biggest barriers to international growth, what AI can realistically solve today, and why data quality has become more than an SEO challenge. In a world where AI increasingly decides what shoppers see, product data quality directly impacts visibility, discoverability, and revenue. The full recap is in the first comment👇 #BalkanEcommerceSummit #ConnectingBalkans #CrossBorderEcommerce #AI #ProductData #DyverAI