Pallavi Kalambe

How do you reimagine product discovery when your catalog spans 1.2B+ items? Suresh Kumar and Balu Chaturvedula recently shared how AI and deeper contextual intelligence are transforming the work associates do to enrich product listings. The result: more relevant, intuitive recommendations that help customers find exactly what they need, faster: https://lnkd.in/gPYm2jaH

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👉 “RAG isn’t one thing—it’s evolving…. into 8 powerful architectures that shape how LLMs interact with knowledge.” Here’s the lineup: 1️⃣ Naive RAG – Embed query → retrieve from vector DB → generate. 2️⃣ Multimodal RAG – Expands to text + images + other data sources. 3️⃣ HyDE (Hypothetical Document Embeddings) – Generate a pseudo-answer → embed → retrieve. 4️⃣ Corrective RAG – Cross-checks retrieval, fact-checks via web/external sources. 5️⃣ Graph RAG – Uses structured graph DBs alongside embeddings. 6️⃣ Hybrid RAG – Merges graph DB + vector DB contexts for richer context. 7️⃣ Adaptive RAG – Multi-step reasoning chains that refine queries. 8️⃣ Agentic RAG – Agents with memory, planning, and tool orchestration. Of what i am seeing in the industry, I see organizations moving step-by-step from Naive → Adaptive → Agentic RAG as their systems mature. The big shift? Moving from “retrieving documents” to “reasoning and acting” across data ecosystems. 💬 Which of these RAG architectures do you think will become the enterprise default in the next 12–18 months? #GenerativeAI #RAG #AIArchitecture #LLM #Innovation

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🧠 Dashboards don’t answer questions — analysts do. The breakthrough came when we stopped asking “what happened?” And started asking “why this segment?” Segmentation turns noise into direction. #DataAnalytics #CustomerSegmentation #AnalyticsMindset #EcommerceData #DecisionMaking

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It’s been a week of strong momentum with our second major media feature now live in The Times of India. In his story, Sujit John captures Walmart’s AI journey through conversations with Suresh Kumar and Balu Chaturvedula, showcasing how our India teams are advancing contextual understanding and personalization at scale. The article outlines how GenAI is enriching 1.2B+ product listings with greater accuracy and completeness, transforming product discovery for customers. Work that once required weeks of manual effort now happens in days, driven by smarter automation and deeper contextual models. Suresh Kumar notes, “Understanding context leads to better recommendations.” And Balu Chaturvedula adds that richer, more relevant catalogue content is helping customers make effortless, informed choices. The feature also highlights Wibey and Sparky, key internal platforms accelerating developer productivity and strengthening our tech-led transformation. Read more: https://lnkd.in/dWUUbgDD #TimesOfIndia #TeamWalmart #Leadership #Innovation #Technology

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Kroger has taken a step by consolidating Media, Loyalty, and Insights under RMN, positioning Kroger Precision Marketing #KPM as a comprehensive growth engine for brands. This new structure emphasizes the integration of data science and technology at its core. For more details, visit the full story at [https://lnkd.in/eFmFfd_q). #Kroger #Marketing #DataScience #Technology Path to Purchase Institute Progressive Grocer

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I am sharing two recent projects that examine the intersection of data analysis and organizational decision-making. These repositories focus on creating realistic business scenarios to test the practical application of SQL and dashboard design. My focus wasn't just on processing the data but on how we can use it to answer high-priority questions and provide clarity in complex scenarios. 1️⃣ Operational Risk Mitigation Strategy (United Air Lines) To make this more than just a technical exercise, I used Gemini as a Stakeholder Simulator to trigger a high-priority request from a fictional COO. The Focus: Addressing cascading delays and morning hub congestion using SQL. The Deliverable: I prepared an Executive Summary and Quantified Findings report—focusing on how data can be presented to leadership to support risk mitigation. 🔗 Flight Delay Analysis: https://lnkd.in/d-i2qDKT 2️⃣ Customer Segmentation Analysis & Dashboard (Online Retail RFM) A deep dive into customer behavior using the RFM framework, complemented by a functional dashboard architecture designed for different business needs: Executive Summary: High-level KPIs and sales trends for a glance at business health. Global Segment Breakdown: Clear distribution of RFM segments and revenue. Regional Dynamics: Monitoring Average Order Value (AOV) across geographic markets. "The Microscope": A granular tactical tool for micro-segmentation and exporting targeted marketing lists. The part of this work I enjoy most is the challenge of taking raw numbers and translating them into a clear narrative—and functional tools—that help a team decide on the next steps. 🔗 RFM Segmentation: https://lnkd.in/dTfY9Vqa I’d love to hear your thoughts or any feedback you might have on these approaches! #DataAnalytics #SQL #BusinessIntelligence #CustomerInsights #StakeholderManagement #DashboardDesign

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In my recent Panaversity assignment, I studied how Agentic AI is driving transformation in global enterprises and Walmart offers a compelling demonstration of its impact. Walmart operates thousands of retail locations globally, requiring massive coordination between employees, inventory systems, and customer support teams. Routine tasks such as shift scheduling, customer inquiries, and equipment maintenance were taking significant time and resources. Manual shift planning alone took around 90 minutes per manager per day, while customer service teams struggled to keep up with growing query volumes. The Agentic AI Solution: Walmart deployed intelligent AI Super Agents — a network of AI assistants that automate employee scheduling, monitor equipment performance, assist customers through chat, and even generate insights for store managers. These agents take inputs such as store data, staff availability, customer queries, and inventory levels, then output real-time recommendations, alerts, and automated actions. Humans remain in control, approving key decisions and overseeing the AI’s performance. The Results: 40% faster customer query resolution Shift planning time reduced from 90 to 30 minutes 30% fewer emergency maintenance alerts 19% reduction in refrigeration maintenance costs Significant operational savings and improved employee efficiency This case study highlights how Agentic AI empowers human teams rather than replacing them, enabling smarter, faster, and more cost-effective business operations. Source: Walmart AI Operations Case – Retail Dive, Business Insider, and Walmart Global Tech Blog #Panaversity #AgenticAI #Walmart #AIinRetail #ArtificialIntelligence #Automation #DigitalTransformation

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