Srini Reddi

AI Success Starts with Data Quality and Governance A recent TechCrunch article reinforces something many of us driving digital transformation in large organizations have learned firsthand: better-quality data, not just more data, is the real driver of successful AI initiatives (https://lnkd.in/eHR9kcXH). Companies like iMerit are showing that domain-specific, expert-labeled data consistently outperforms massive, loosely curated datasets. But that precision doesn’t happen on its own. To build trusted, explainable, and scalable AI systems, enterprises must pair high-quality data with strong data governance practices: 🛡️ Ensure compliance and auditability 🔍 Maintain data lineage and integrity 🔄 Support continuous validation and improvement 🔗 Align data with business context and use case From my experience leading digital transformation, I’ve seen how data governance transforms AI from a prototype into a platform, one that can be measured, trusted, and scaled. As organizations race to adopt AI, success won’t be determined by model complexity, but by how well you manage the data that feeds it. #AI #DataQuality #DataGovernance #DigitalTransformation #EnterpriseAI #ResponsibleAI #TechLeadership

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We are living through one of the most profound shifts in human history - the evolution of intelligence itself. In this era of AI, World Models, Quantum and embodied AI, I’m excited to engage with the thinkers and builders who are not just observing the change, but actively shaping it. Looking forward to meaningful conversations, bold ideas, and collective learning at the event.

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"Shoppers are strategically shifting toward more gift card purchases to maintain holiday gifting traditions amid economic pressures, according to new research from Blackhawk Network (BHN), a leading global provider of branded payment solutions. BHN's annual holiday forecast, which surveyed over 2,100 U.S. consumers, reveals that while 13% are purchasing gifts for fewer people due to financial constraints, overall holiday gift spending continues to rise and gift cards will represent 39% of total holiday budgets—a 12% increase over last year." https://lnkd.in/gQxrpy35

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The experience economy is booming — and smooth, secure payments are key to winning customer loyalty. In this USA Today piece, our CPO Bob Legters explains how solutions like Skrill and #PaysafeCard are meeting evolving consumer needs. Insightful and relevant for any business in the digital space. #CustomerExperience #ECommerce #Payments #ItStartsHere

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The ill effects of a modern Ai technology being used in unethical ways is real and the consequences are indeed unimaginable.

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Our CTO Deepak Dastrala talks about intellectAI's mission to drive efficiency in BFSI with AI-first products and more.. Great read!

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When we started Fynd in 2012, retail was deeply fragmented. Inventory, orders, and customer data lived in separate systems, and brands were forced to operate without a unified view of their business. We began by solving this challenge. Over time, integration became the baseline. The real opportunity was building a single operating layer across catalogue, inventory, logistics, and customer experience. Not just systems that connect, but systems that coordinate. Today, AI is accelerating that shift. For me, its value is not in dashboards or surface features. It lies in intelligent execution, predicting demand, automating workflows, personalising journeys, and reducing operational issues at scale. As we expand into markets like Middle East and UK, I am reminded that these challenges are universal. Brands everywhere are navigating complexity and disconnected systems. What they need are coherent platforms with intelligence embedded at the core, not layered on later. I recently spoke with Neil Ainger at Retail Technology Innovation Hub about this journey, our global expansion, and how I think about AI-powered unified commerce. Scott Fynd Farooq Ronak Ragini Salman Jigar Kushan Aditi Nanditha Rixon Pinto Vipul

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The agent patterns that matter in production There are lot of agent patterns and designs being discussed. Once agents are deployed into real products for supporting customers, teams, or business workflows, the conversation changes quickly from experimentation to reality. The question is no longer what’s possible, but what’s reliable - In production environments, most agent designs converge around a small number of architectural decisions: 1. How much autonomy the system actually needs? 2. Where determinism is preferable to dynamic reasoning? 3. How cost, latency, and quality are controlled over time? Multi-agent designs can help in specific scenarios, but they often introduce coordination and observability challenges that outweigh their benefits. These are not model-selection problems, they are system design and operating model decisions. At a practical level, production agents typically center on following patterns: 1. Orchestration: how work is routed, sequenced, or planned (Example: choosing between deterministic workflows vs. dynamic planning) 2. Tool Use & Integration: how agents interact with real systems and data (Example: APIs, data platforms, or enterprise services) 3. Multi-Agent Design: when responsibilities are split across agents (Example: reviewer–executor setups, or role-based separation) 4. Memory & Retrieval (RAG): how context and knowledge persist over time (Example: short-term state vs long-term knowledge bases) 5. Guardrails: how behavior is constrained and risk is managed (Example: policy checks, approval steps, or deterministic fallbacks) 6. Evaluation & Monitoring: how quality, cost, and drift are measured (Example: detecting when an agent’s answers slowly degrade) Most real systems combine several of these. An agent might use routing to decide which tool to call, hit a guardrail that requires human approval, then log everything for evaluation. The patterns overlap and connect. Over the next few posts, I will walk through each of these patterns and we will understand what they solve and how they are used, and how to think about them from a production and leadership perspective. The goal isn’t to reduce complexity only, It’s to make agent systems operationally sound, measurable, and accountable. #AIAgents #LLM #MachineLearning #CloudArchitecture #EnterpriseAI

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Smarter Journeys Begin With AI in Retail Bots AI is everywhere in retail - but is it truly working for your brand, or just checking a box? At CrossML Pvt Ltd, we built a next‑gen AI Customer Support Bot that does more than talk: it thinks, learns, and sells - all while staying budget- and system-efficient. It’s turning support into sales, automation into loyalty, and casual chat into genuine growth. Why retailers - fast-growing and established - are betting on AI bots: -91% of retail IT leaders aim to make AI their top tech investment by 2026. -GenAI chatbots drove a 15% boost in Black Friday conversions . -60% of shoppers noticed better forecasting and inventory with AI retail tools . -By 2025, 85% of retail interactions will be fully automated. AI-powered support yields a 25% increase in customer satisfaction . 🛠️ How Our Bot Drives Smarter Journeys 1. Conversational AI that Feels Human & Converts Built with GPT‑4, Gemini & LLaMA - understands tone, intent, context, and nudges cart abandoners seamlessly. Impact: 63%+ retailers saw conversion gains, 55% saw higher average order value . 2. Seamless Backend Integration Connects Shopify, WooCommerce, CRMs, POS, loyalty & delivery APIs - provides real-time, personalized support. 3. RAG-Powered Real-Time Accuracy Fetches live data to answer so there’s never outdated or inaccurate info - drastically reduces hallucinations. 4. Empathy at Scale with Sentiment Analytics Our dashboard tracks neutral-to-negative sentiment and flags chats needing human attention - balancing speed with emotional intelligence. 🚀 Real-World Results ✅ 50% faster first responses during peak events ✅ 18% lift in daily sales through personalized recommendations ✅ 20% lower support operating costs ✅ 30% drop in repetitive queries ✅ 25% higher CSAT scores 🔮 What’s Next for Retail Bots - Voice & Visual Interactions - think kiosks, in-store voice support, and image-based assistance. True Omnichannel Presence - across live chat, email, WhatsApp, and more. Industry-Specific AI Modules - tailored to fashion, electronics, beauty, grocery - with domain logic built-in. Predictive Support - anticipating issues, identifying churn, and initiating outreach before customers even ask. At CrossML, our AI bots aren’t just hype - they’re built for real-world impact. If you’re a CTO, Founder, or AI Lead ready to elevate your retail strategy, let’s connect. Let’s make AI work smarter - for you and your customers. #RetailAI #CustomerExperience #ConversationalAI #GenAI #RAG #Omnichannel #CloudNative #CrossML

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