Nimble

C-Suites betting on GenAI expect seamless automation and real-time intelligence. ?????? ?????? ??????????, ???? ???????????????? ??????????, ??????????, ???? ????????-?????? ????????. Not because the models don’t work— But because the data behind them can’t keep up. For years, enterprises built AI on ETL (Extract, Transform, Load)—a pipeline designed for structured, rule-based systems. But LLMs and GenAI aren’t rule-based. They’re adaptive. And they need something ETL was never built for: real-time, flexible, high-volume data. That’s why ELT (Extract, Load, Transform) is winning. ?????? ????. ??????: ?????? ???????????????????? ???? ??????????-?????? ? ETL restructures data before storage, creating latency and blind spots. ? ELT ingests raw data first, ensuring AI models adapt in real-time. ? ETL was designed for fixed, rule-based analytics. ? ELT enables dynamic, AI-driven decision-making at scale. AI doesn’t fail because it’s unreliable. It fails when it’s forced to learn from outdated, pre-filtered snapshots. Informatica being named a leader in the 2025 Gartner Magic Quadrant is proof positive that ???? ???? ???????? ???? ???????? ???? ?????? ???????? ???? ???????????? ????????. So here’s the question: Are you still running AI on an ETL pipeline built for the last decade—or adapting for the next one? #AI #DataQuality #EnterpriseAI #DataInfrastructure #LLMs #ETL #ELT

AI search is lying to you—and MIT Professional Education ’s proved it. According to recent research from MIT, Google’s AI Overviews are ?????????????????????????? ????????????????, ???????????????????? ??????????????. It’s not the only one either—according to Tom’s Guide, Gemini is confidently ???????????????????? ?????????? ?????????????????????? that was correct months ago but is now useless. However, AI search isn’t failing because models aren’t good enough. It’s failing because retrieval pipelines aren’t designed for real-time updates. The reality is that retrieval-augmented generation (RAG) isn’t just about adding a retrieval step. It’s about ensuring that retrieval is continuously updated, structured, and relevant. Nimble fixes this. Instead of relying on stale embeddings or pre-indexed documents, Nimble functions as a real-time external data pipeline that can feed AI search engines with the freshest, most relevant information—so outdated data never leads to irrelevant answers again. The next generation of enterprise AI search will be won by companies that move beyond static RAG implementations and embrace real-time external data pipelines as a standard. So, AI engineers and product leaders—how are you keeping your AI search results from going stale? #AI #EnterpriseSearch #AIProduct #RAG #LLM

What happens when Chris Perry and a group of senior retail leaders sit down for an off-the-record dinner? You get the kind of conversation that doesn’t happen on stage. We hosted a roundtable dinner discussion, led by Chris Perry, Chief Learning Officer at firstmovr—alongside a powerhouse group of e-commerce and retail experts. We dug into what’s really slowing down digital commerce—insight delays, execution gaps—and one common theme was clear: data is still being filtered manually through too many layers before anyone can act. The takeaways surprised us. The solutions were even sharper. We’ll be sharing a few of them next—along with a resource from the night that’s already helping teams rethink how they operate. Grateful to Chris and everyone who joined!

Most AI waits for human input. Agentic systems don’t. Today’s digital commerce is largely reactive. Dashboards are refreshed, reports are shared, actions are queued. Even with AI-powered automation, workflows still get bottlenecked by the need for a human to interpret, approve, and act. Agentic Commerce takes a different path. AI agents observe, evaluate, and act continuously: adjusting pricing, inventory, and promotional decisions in near real-time, without waiting on manual triggers. But, shifting to an agentic workflow isn’t as simple as making a few tweaks to your reporting. You need real-time, external data infrastructure that is built for autonomy rather than just analysis. That’s what Nimble is focused on: delivering the data layer that lets agents operate independently of dashboards, latency, or scheduled syncs. We’re exploring what this unlocks—and how it reshapes execution—at #ShopTalk Spring 2025. Full write-up here → https://t2m.io/QmGxgAV #AgenticCommerce #RetailTech #AIinRetail #DigitalShelf

Dashboards are dead. Digital shelf analytics has failed to deliver. So what’s next? At #ShopTalk2025, we heard the same frustrations over and over: — Siloed data. — Static reports. — Slow decisions. The future isn’t another dashboard. It’s a new infrastructure—?????????????? ???? ???? ???????????? ???????? ???????????? ?????? ?????? ???? ???????? ????????. This is what we call ?????????????? ????????????????: Where retail strategy adapts at the speed of the shelf. ?? Read more on how we’re helping teams make the shift: https://t2m.io/QmGxgAV PS. If you are around at ShopTalk, say hi to our team | Uriel Knorovich | Michael Corry | Josh Wood | Menachem Salinas | James Ryan?? #RetailTech #AgenticCommerce #ShopTalk #AIAgent

Unauthorized sellers are shifting your brand’s positioning in real-time. They undercut pricing, manipulate product listings, and flood the market with inconsistent messaging. By the time brands notice, the damage is done. Legal takedowns and manual enforcement can’t keep up with sellers operating at scale. That’s why enforcement needs to be proactive. AI-driven brand protection monitors, detects, and enforces standards before revenue is lost. With it, you can: ?? Identify unauthorized sellers the moment they appear ?? Detect price manipulation before customers see it ?? Enforce compliance across every major marketplace We mapped out the entire enforcement lifecycle to show what proactive brand protection actually looks like—from monitoring to detection to automated enforcement. Because unless you understand where enforcement breaks down, it’s almost impossible to scale it effectively. So how much control do you really have over your brand’s presence online? ???????? "???????????? ????" ???? ?????? ???????????????? ???? ?????? ?????? ???????? ???????????? ???????? ???? ?????? ?????????????? ???????????? ?????? ???????????????? ???????????????????????? ?????????????? ??????????. #BrandProtection #eCommerce #RetailTech #AI #UnauthorizedSellers

Static dashboards were built for a retail industry that stopped existing 10 years ago. Today’s digital shelf evolves by the hour across marketplaces, regions, and sales channels. In this market, static dashboards are nothing but a bottleneck—yet, many retail teams still rely on periodic reports to guide real-time decisions. The future of digital shelf strategy is dynamic, AI-driven, and omnichannel-ready. Brand and eCommerce teams need real-time intelligence that reflects what’s happening now, not what happened last week. With Nimble , retail teams can move beyond static, slow reporting and unlock real-time intelligence that fuels smarter, faster, and more agile execution. By integrating external data with AI, Nimble allows retailers to get instant access to market updates as they happen. It’s not a replacement for expertise—it’s infrastructure that allows expertise to scale. We’re exploring this—and what’s really happening across your store—at #ShopTalk Spring 2025. Our CEO (Uriel Knorovich) and Nimble team (Michael Corry Josh Wood Menachem Salinas James Ryan) will be there. Are you attending? Let’s meet up! #RetailAI #DigitalShelf #RetailIntelligence #CPG

Retailers are losing billions because they are focused on the wrong things and looking for solutions in the wrong place. CB Insights estimates $1T is lost annually due to bad inventory decisions. But here’s the uncomfortable truth: forecasting models aren’t failing because they’re broken; they’re failing because they’re blind. Nimble 's Retail Intelligence Flow reveals a telling pattern: ? Forecasting errors → lead to stockouts and overstock. ? Missed market opportunities → delay activation. ? Poor customer experiences → erode brand loyalty. Most retail decisions are built on stale, internal data that doesn’t reflect real-time consumer demand. Every decision—pricing, inventory, promotions—starts at the source. If your data is outdated, your strategy is already behind. That’s where real-time external data makes a real distinction: ? Market activation happens faster when external demand signals flow into decision-making. ? Pricing becomes dynamic when it reflects real-time consumer behavior. ? Forecasting gets sharper when external data fills in the gaps internal models can’t see. Retail leaders—are you ensuring that your data flows at the speed of demand? #RetailIntelligence #DemandForecasting #RetailData #AI #MarketActivation #DataDriven

Retailers trust AI for pricing and demand forecasts. But what if the DATA feeding the AI is lying? ?? AI-powered pricing sounds excellent until it’s running on last month’s competitor pricing data. Suddenly, your "optimized" discounts are eating into margins instead of boosting revenue. Not so exciting anymore, right? Same goes for demand forecasting—How can AI make the right decisions if it’s learning from last quarter’s trends? Retail AI models require ????????-???????? ???????????????? ???????? to remain effective. Without it, pricing models misfire, demand estimates fail, and client personalization falls short. If your AI isn’t adapting to today's market signals, is it truly “learning”? How do you navigate #RetailAI crisis? ????

A friend of mine was shopping lowes.com the other day and saw small cardboard moving boxes priced $1.75. His wife swore it was only $1.38. They argue and… she was right. But.. plot twist: he was also right. They compared screenshots. It turns out, depending on your browser settings, Lowe’s dynamic pricing can swing dramatically within hours. One moment, it’s $1.75; the next, it’s $1.38. In the end, he even decided to check the shelf price in-store before accepting his online order. Retailers like Lowes adjust prices multiple times a day based on: — Real-time demand — Inventories — Regional factors If what you’re using in your agency isn’t reflecting prices in real-time, by the time you catch a pricing war on paper, your client has already lost market share. Static tracking isn’t enough anymore. It’s time to adopt AI-powered, real-time competitive intelligence to capture every price shift as it happens. Bottom line: Live analytics make your pricing bulletproof. No more second-guessing, just spot-on strategy. #realtimedata #retailstrategy #pricing #ecommerce #agencies Which pricing intelligence platform do you use? Let’s all share for everyone to know down below ??