Nimble

In one ZIP code, the same SKU was selling at 4 different prices. When growth tightens, even small mismatches move carts. Holiday growth is ~+3.6% YoY (Mastercard). Across 500+ SKUs, even minor regional drift becomes material unless it’s caught in-run. Nimble’s Web Search Agents → Run real-time price & availability checks by ZIP → Surface PDP drift before rank moves → Alert the right owner for fast fixes ✅ Ops brief: https://lnkd.in/d3fCZhJJ Or connect in Slack: https://lnkd.in/dgdci6KD

In prestige beauty (think ~$120 holiday gift sets), what converts are the trust signals shoppers see on their local page. Your plan is approved, components verified, assets shipped. The runtime takes over and pages start to differ by market. You start to see issues in the little slips: a missing “Limited” badge in Miami, a thin BNPL label in Chicago, a ship-by clock off by days in LA. National checks can be green while ZIP-level variance hides. Those small misses reduce trust and stall carts. Nimble’s Web Search Agents verify those cues by ZIP and route the drift to the owner so the page your shopper sees matches the promise you made. If you want this run on your holiday SKUs, ping us on Slack: https://t2m.io/KwySiTG

NYC already feels like holidays. So did Georgia Danzger and James Ryan's notes on the way back from Javits. In meeting after meeting, the same split showed up: The plan lived in Databricks. The runtime lived in browser tabs. Brand and shopper insights teams described the same scene in different ways. Screenshots of missing promo badges in Slack. PDP tabs open to check whether hero images and bundles matched what got approved. Pricing and RGM partners asking for a quick look at a few key markets, even though the latest review export was from last week. The AI and reporting stack is there. The live web input still leans on manual checks. Our Web Search Agents plug into Databricks and turn those live pages into structured tables. PDP content, badges, prices, availability, and other signals land where teams already work, so those checks move from browser tabs into the background. As the season builds, teams see where reality drifts from the plan that day and fix it in-run instead of discovering it in the recap. If we missed you at Databricks Data + AI World Tour and you are working on that web input layer in Databricks, Georgia Danzger and James Ryan are happy to walk you through the same flow they shared onsite. - https://lnkd.in/e_dPsH29 #DataAIWorldTour

Most “AI shopping assistants” still answer like a blog post, not a buyer. It comes down to the data layer. They don’t actually know what’s in stock, what’s on promo, or what changed overnight. APIs give you structured data, but only for what they expose. LLMs synthesize nicely, but they’re stuck with whatever was true at training time. When we built a simple “find the best pizza 🍕 near me” demo, it surfaced the same constraint we see in retail every day: humans have judgment but no scale, APIs have scale but no adaptability, and AI has reasoning but no live connection to the shelf. Our Web Search Agents add that missing layer: agents that browse the live web, extract structured fields, validate freshness, and stream clean JSON into your AI stack so your AI answers from live data, not stale snapshots. We turned that pizza demo into a short breakdown. 👉 https://lnkd.in/dY_9nnuH Run it live on your own SKUs. 👉 https://lnkd.in/dakpKSSQ

Most AI roadmaps now include a slide called “Data Classification.” Few teams talk about what those labels are actually sitting on. Classification is how teams bring structure and governance to messy data: from product pages and reviews to transaction records. In practice, that layer often runs on partial, outdated slices of the truth: last month’s exports instead of live PDPs, a narrow slice of reviews rather than the full stream, and internal records with little context from the open web. When the underlying data is this thin or out of date, you do not just get noisy labels; you lose the trust and auditability classification was meant to create. Nimble’s 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁𝘀 are built to close that gap. They turn live web pages into structured inputs your classification layer can govern and audit with confidence. They capture full product, price, and content context across retailers, keep labels in sync with what is actually on the shelf and on the page, and keep your AI and governance workflows fed with continuously refreshed external data. 📍If you are heading to Databricks Data + AI World Tour in Chicago on November 19 and working on data classification, governance, or external data pipelines, come find our own Josh Farber. He will be there to show how real-time web data turns classification from a static checklist into a live control layer. 📌 If you will not be there but want to run this on your own classification or governance stack, you can reach us on Slack: https://lnkd.in/dDkv3fSP 👋 See you there

CPG teams extract over 1B rows of eCommerce data every week. Most of it lands too late to act. Your dashboards are full and your timing is off. Your scraping vendor runs on their schedule, but shelves change on theirs. By the time you see that a competitor dropped prices or your product went out of stock, the moment to respond has passed. In London, Alban Camaj and Joe Morell showed how brands are solving this with our Web Search Agent Platform. Next stop: Chicago on November 19 at the Databricks Data + AI World Tour. Nimble’s Web Search Agents feed live, structured web data into Databricks, so teams can see shelf changes as they happen instead of waiting for the next vendor refresh. If you're still working off last week's data, your competitors probably aren't. Get a quick walkthrough: https://t2m.io/KwySiTG

Dove just launched a Holiday Treats Collection with the Radio City Rockettes. Six festive scents, exclusive at Target and Walmart Big footprint, high expectations. For big seasonal launches, a few details decide how the shelf looks in real life: ➡️ Scent lineups can change by retailer ➡️ Bundle claims can drift between PDP and shelf ➡️ Availability and MAP can shift by ZIP In week one, it’s easy to assume it’s all working. The shelf doesn’t always agree. Our Web Search Agents run ZIP-level checks across retailers so teams see what’s live, what’s missing, and where to act before it shows up in the recap. ⤵️ Comment “holiday win” if you’d like a quick walkthrough of how we track launches like this end to end.

Running a holiday LTO? The shelf clock starts the moment it goes live. This season, Chex x Peanuts holiday boxes are back ⤵️ Limited-edition cereal with a “buy 3, get the tin” promo. For LTOs like this, the first 10–14 days drive most of the volume. On paper: product in the planogram, copy live, price tagged. In stores: by Week 2, some locations are already thin or out. Holiday LTOs crest fast; standard weekly reporting shows the outcome, not the on-shelf moments you can still influence. For LTOs, we run daily shelf reads in Q4: in-stock flags, price chips, promo copy, so category and omnichannel leads see where the shelf is slipping from the plan while there’s still time to react. Otherwise, a holiday LTO plan turns into “promo, no product.” What cadence do you trust for LTO shelf reads in those first two weeks?

Walmart's Black Friday runs in two waves. Wave 1: Walmart+ early access (members-only). Wave 2: Public launch. Most roadmaps anchor to the public drop. The sales line is written in Wave 1. When timing slips in Wave 1, lift leaks. Last season, we saw SKUs marked “live” nationally while Wave 1 ZIPs showed “coming soon.” Approvals were set; timing wasn’t aligned. RGM teams that sync to Wave 1 verify badges, MAP, and price chips by ZIP before the gate opens, and route any drift in-run. Nimble’s Web Search Agents run those ZIP-by-ZIP checks ahead of and during early access. Access → Activation → Sales. What’s your Wave 1 KPI? ⤵️ Want your assortment to show up on time? Ping us on Slack: https://lnkd.in/d_67ccDw

We’re back from London. Next stop: NYC. Tomorrow our team will be at the Databricks Data + AI World Tour! We help AI & data teams access 𝗹𝗶𝘃𝗲, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘄𝗲𝗯 𝗱𝗮𝘁𝗮 through our 𝗪𝗲𝗯 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺. If you’re building on Databricks and need real-time pipelines that just work, let’s connect. We’ll be at Javits Center North in NYC. If you see us, wave 👋. If not, DM Georgia Danzger and James Ryan. #DataAIWorldTour