Zenyt.ai

Welcome to the team ✨ We’re excited to welcome Julien Bergametti to Zenyt as our new Business Development & Go-To-Market lead. Before joining Zenyt, Julien worked across finance and early-stage startups, where he developed deep expertise in financial modeling, market dynamics, and scaling businesses from zero to traction. Operating in high-performance environments shaped a structured, results-driven approach to growth and a strong understanding of what it takes to turn opportunity into real revenue. Julien brings a rare mix of analytical rigor and entrepreneurial instinct. He holds a Master of Science in Engineering from UCLA, where he specialized in finance and data analysis, building a strong foundation at the intersection of data, strategy, and business growth. At Zenyt, Julien will lead Business Development and Go-To-Market, helping accelerate the company’s next stage of growth. His focus will be on building strategic partnerships, structuring scalable acquisition systems, sharpening our positioning, and expanding Zenyt’s presence across the e-commerce ecosystem. His mission: turn Zenyt’s AI capabilities into measurable revenue impact for e-commerce brands. We’re incredibly excited to have him join the team and help shape what’s next for Zenyt 🚀 Welcome Julien!

If you don’t know about these 7 AI shifts, you’re already behind. AI is already changing how ecommerce teams ship, test, and fix their stores. Swipe through to learn the 7 signals every ecommerce leader should know.

Join us in NYC! Zenyt is expanding and we’re hiring two new interns across Product and ML Engineering. We’re looking for two talented and motivated people who are eager to learn, move fast, and build meaningful things. If you’re interested, or know someone who might be, explore the roles and apply here: https://lnkd.in/edVu6vVT Please include your resume and a short answer to the following questions: • Why do you want this internship? • Tell us about a project or initiative you started or contributed to significantly, not because you were required to, but because you were genuinely driven by curiosity, passion, or the desire to make an impact. We look forward to hearing your story. Feel free to reach out to Arthur Pentecoste, Raphael Rozenblum, or Adam Azoulay if you’d like to know more!

There’s a simple way to boost this product page’s sales. Our AI spotted a revenue opportunity on a TORY BURCH PDP. It’s a small adjustment with real impact. Can you spot it too? Answer’s in the comments but try first 👇

Welcome to the team ✨ A new season at Zenyt also means new people joining the adventure. We’re very happy to welcome Dimitri Abou Issa and Tom Amirault, who will be spending the next six months with us. Dimitri Abou Issa is an EDHEC student and former VC analyst. Before joining Zenyt, he cofounded TicketNunc, a consumer app that reached 250,000 downloads. At Zenyt, he’ll be working at the intersection of product and growth, helping us better understand how users interact with the product and how we can improve discovery, delivery, and conversion across the platform. Tom Amirault is a Franco-American engineer from CentraleSupélec. Over the past year, he built applied ML systems for companies like Air France, RTE, and Airbus. At Zenyt, he’ll be working on the ML pipelines behind our AI agents, from LLM orchestration to computer vision and evaluation systems that help us detect issues on e-commerce sites more reliably. Very excited to have you both with us. Welcome to Zenyt. If you want to join the team and work with the coolest view of NYC, discover our open roles here: https://lnkd.in/edVu6vVT

$100k-$300k in revenue recovered. By seeing what customers actually see. That’s what New York & Company achieved after deploying Zenyt’s AI agents to monitor their store end-to-end, just weeks before Black Friday. Completing a major migration from Hydrogen to Liquid right before BFCM was ambitious. The team was confident. But with 100+ SKUs refreshed and thousands of pages updated, the QA burden was massive. The reality every fashion retailer knows: there’s no realistic way to manually browse every page, every collection, every variant. Issues were out there, mobile conversion blockers, promo code inconsistencies, technical SEO gaps, they just had no visibility into what customers were actually experiencing. By letting AI agents monitor their store like millions of real shoppers, New York & Company gained full site visibility heading into their most critical selling period. Within days, Zenyt identified 500+ opportunities across mobile UX, promotional integrity, crawlability, and product accuracy issues the team had no realistic way to catch manually. The result: 50 priority fixes deployed before Black Friday traffic surged, a 90% reduction in QA effort, and an estimated $100k–$300k annual impact from conversion improvements. Laura Cantor, VP of Marketing & E-commerce at New York & Company, shares the full story and why we’re all failing calculus together in this week’s DTC Podcast episode. Listen to the full conversation: https://lnkd.in/e9ZUcQdS Read the complete case study: https://lnkd.in/efi7rSr8 Thank you Laura Cantor for sharing your story! #Ecommerce #DigitalTransformation #AI #RetailTech #BlackFriday #EcommerceStrategy #CustomerExperience #ConversionOptimization #DTC #RetailInnovation

Heatmaps show you where customers click. But they don’t tell you why they stopped buying. Heatmap tools are nice, you can see where people click, where they scroll, where they go. But then they leave you to figure out what it actually means. You’re staring at this colorful map thinking: “Okay, so they’re clicking here... but what does that red zone actually mean? Is this good or bad?” The data is there. But you’re the one who has to figure out what it means. And honestly? That takes time. Hours of analysis. Meetings to debate what those clicks actually tell you. Weeks before you can connect the dots between “customers clicked here” and “this is costing us conversion.” Meanwhile, your competitors are fixing issues while you’re still trying to interpret the heatmap. I’ve seen teams spend days analyzing heatmap data, trying to understand where customers go and what they’re scrolling past. By the time they figure it out, the problem has already cost them sales. The question isn’t whether heatmaps are useful, they are. The question is: can you afford to wait while you manually figure out what all that click and scroll data actually means? At Zenyt, we use AI to do the heavy lifting. Our agents monitor your store like real shoppers and automatically interpret what’s happening. No hours of analysis. No guesswork. Just: “Here’s what’s costing you the most, and here’s what to fix first.” Because heatmaps show you the data. But you need someone or something to tell you what to do about it. #Ecommerce #ConversionOptimization #Heatmap #UserExperience #AI #EcommerceStrategy #DigitalCommerce #CustomerExperience #DataAnalysis #RetailTech

CONSISTENCY = TRUST = VISIBILITY Most brands are not losing sales because of bad products or slow sites. They are losing sales because their data cannot agree with itself. When someone asks ChatGPT or Perplexity “Where can I buy X?“, the AI does not just check your homepage. It checks your PDPs, FAQ, shipping info, support content, reviews, and third party sites. If that information does not match, the AI often skips you. Not because you are a bad brand. Because it cannot verify what is true.  Example we see all the time: Someone asks: “Where can I buy this with delivery in 48h?” The AI finds: • Homepage: 48h  • FAQ: 72h  • PDP: 3 to 5 days From the AI’s perspective, this is a trust issue. If it cannot confirm one reliable answer, it recommends a brand where it can. Even if you actually offer 48h delivery. And this happens to strong e-commerce teams. When you have hundreds of SKUs, constant releases, multiple teams updating content, and ongoing promo or logistics changes, manual checks do not scale. The dangerous part: this usually happens silently. No alert. No dashboard. No ticket. Just fewer high intent shoppers being sent your way. AI does not punish bad brands. It filters out inconsistent ones. Winning brands are treating site data like revenue infrastructure: Shipping promises match everywhere. PDP, support, and policy content stay aligned. Third party listings reflect the same source of truth. Because in AI commerce, consistency is visibility. The real question is not: “Do you offer fast delivery?” It is: If an AI audited your digital footprint today, would it find one answer or five? Most teams do not know. The ones who check usually find drift. If you want, I can show you what this looks like on a real brand site. It usually takes a couple of minutes to spot the first inconsistency.

One fix can boost this product page sales, can you spot which? Our AI flagged a revenue opportunity on an Lacoste product page, can you spot it too? The answer is in the comment but try to guess first!

So that post about a BFCM migration experiment we ran with Laura Cantor and New York & Company went viral. Honestly? It wasn’t really about their story. It was about finally being able to say out loud what everyone in e-commerce has been thinking but no one wants to admit. You know that moment, right? You ship a migration or redesign, you do your QA, you cross your fingers... and you just hope nothing breaks. We’ve all been there. And somehow we’ve all just accepted it as part of the job. But New York & Company decided to stop accepting it. The visibility gap doesn’t have to be inevitable. It’s actually solvable. Before BFCM hit, we helped them test thousands of SKUs using AI agents. Not to replace their team but to give them superpowers. And what we discovered? It wasn’t just bugs. It was all the invisible friction that was quietly killing their conversion. Product descriptions that didn’t match the images. Variants sitting in the wrong collections. Those “minor” attribute errors that you think don’t matter, but they’re repeated across hundreds of products. Here’s what changed everything though: the AI didn’t just find issues. It answered the question they actually needed: which ones are costing them the most revenue right now? Turns out, less than 15% of the issues represented the majority of potential revenue impact. That’s the difference between chaos and actually knowing what to do. Instead of getting a list of everything that’s broken, they got a clear answer: here’s what to fix first. Look, e-commerce isn’t getting simpler. Teams aren’t magically getting bigger. And manual QA? It just doesn’t scale anymore. But that visibility gap? That’s actually fixable. I think the post resonated because it pointed to something we all need: a practical way forward. Not more manual work. Not another dashboard to stare at. Just clear visibility into what your customers are actually experiencing, prioritized by what actually moves the needle on revenue. The brands closing this gap first? They’re not just winning, they’re operating with a level of confidence their competitors don’t have. If you’re shipping updates without knowing which issues are costing you the most revenue, maybe it’s time we change that. #Ecommerce #ConversionOptimization #AI #DigitalCommerce #EcommerceStrategy #RetailTech #QA #CustomerExperience