National Payments Corporation Of India Npci

🗂️Caching Strategies in Java — and when to actually use them Most developers understand what caching is, but fewer know which strategy to choose when. Here’s a breakdown I wish I had early on: 1. Cache-Aside (Lazy Loading): The application checks the cache → miss → fetches from the database → writes to the cache. String val = cache.get (key); if (val == null) { val = db.fetch (key); cache.put (key, val); } ✅ Great for read-heavy workloads ⚠️ First request always hits the database (cold start problem) 2. Write-Through: Every write goes to the cache and the database simultaneously. ✅ Cache is always consistent ⚠️ Higher write latency — you're paying the cost upfront 3. Write-Behind (Write-Back): Write to the cache first and asynchronously flush to the database later. ✅ Ultra-fast writes ⚠️ Risk of data loss if the cache goes down before the flush 4. Read-Through: The cache sits in front of the database and loads data on a miss automatically (e.g., via Spring's @Cacheable). @Cacheable(value = "users", key = " #id ") public User getUser(Long id) { ... } ✅ Clean abstraction, great with Spring Boot ⚠️ Stale data risk without proper TTL configuration 5. Refresh-Ahead: Proactively refreshes entries before they expire. ✅ Near-zero latency for hot data ⚠️ Complex to implement; may cache data you never use Quick decision guide: - Scenario: Read-heavy, occasional writes → Strategy: Cache-Aside - Scenario: Strong consistency needed → Strategy: Write-Through - Scenario: High write throughput → Strategy: Write-Behind #Java #SpringBoot #Caching #SystemDesign #BackendEngineering #Microservices #SoftwareEngineering

The most powerful changes in India rarely arrive loudly. They simply become a way of life. A chai stall accepting a QR code. A commuter tapping through a metro gate. Money moving across cities in seconds. No announcement. Just a billion small moments turning into habit. That’s how a nation moves forward. This film is a reflection of that journey and the purpose that drives us every day at National Payments Corporation Of India (NPCI) Kudos to the incredible team - Devyani Jagdale , Ruchika Chaturvedi , Tanushree Jha , ashish khazanchi , Akriti Sushil Ganju , Darsh Parekh , Prashant Godbole . Innovation with Purpose. Because the best technology eventually becomes invisible. #NPCI #AlwaysForward #UPI

Your sales team is working hard. Just not in the right direction. Teams are busy pitching. Customers are busy looking for clarity. And somewhere in between… deals are lost. The real shift? 👉 Stop leading with what you want to sell 👉 Start understanding what the customer is trying to solve Because better listening → better discovery → better conversions. If this feels close to home, we’re unpacking this in our upcoming session. Join our live webinar on “Why Sales Teams Aren’t Performing” (Sales Excellence) 📅 30th April (Thursday) ⏰ 4 PM onwards Register now & be part of the shift. https://lnkd.in/g3zk4rvw Follow Skill Spring for more such insights. #SalesExcellence #B2BSales #SalesStrategy #SalesLeadership #RevenueGrowth #SalesTraining #CustomerCentric #ConsultativeSelling #LND #LearningAndDevelopment #SkillSpring

Knowledge Graphs for AI Agents - Structured Memory Beyond Vector Stores Vector stores answer what is similar to X. Knowledge graphs answer how X is connected to Y through Z. Agents need both. Most production stacks still only ship one. Here is the shift I am watching this quarter. The first wave of agent retrieval was pure vector plus RAG. It was enough for search-style lookups and single-hop answers. It fell apart on multi-hop questions, entity linking, and compliance-grade audit trails. The numbers say the ceiling is real. On the Stanford STARK benchmark, vector-only retrieval loses 30 to 45 percent accuracy when a question requires two or more hops across documents. The reason is structural. Embeddings preserve similarity. They do not preserve typed relationships. Two connected facts in two different chunks read as unrelated noise. GraphRAG changes the unit of retrieval. Microsoft shipped GraphRAG 1.0 in late 2024 and followed with a performance update in March 2026. The paper now has over 2500 citations on Google Scholar. Neo4j launched Aura Agent as a managed graph memory service. LangChain and LlamaIndex both ship property graph indexes as first-class primitives. The RAG market overall is projected to grow from 2.33 billion dollars in 2025 to 3.33 billion in 2026, a 42.7 percent CAGR, and most of that acceleration is graph-shaped. Why does it matter for builders. One retrieval call returns a connected subgraph, not three disconnected chunks. Company A funded Company B which acquired Product C. That sentence is now a single query, not three. The caveat. Graph extraction adds 3 to 5 times the ingestion cost. Open-ended semantic search on unstructured content still favors vectors. Hybrid stacks, GraphRAG plus hierarchical retrieval, are now the default in enterprise templates. The moment I changed my mind was watching a pure-vector agent give a wrong answer with perfect confidence. The supporting facts were 40 tokens apart in different chunks. Typed edges would have caught it. Which of these 3 patterns matters most for your agent stack in 2026. Multi-hop reasoning. Entity linking. Audit trails. Drop 1, 2, or 3. Follow for daily insights - where blockchain meets AI, one satisfying swipe at a time. #GraphRAG #AIAgents #OpenAI #LangChain #Anthropic #LLM #Chatgpt #Claude #GoogleAI

20+ years in marketing, and the excitement of building something meaningful never changes. Here’s one of best piece of work for NPCI that I have had the privilege to work on. Strategy, storytelling, and scale - bringing all three together is where real marketing impact happens. Proud ! National Payments Corporation Of India (NPCI) Sohini Rajola Kunal Kalawatia Ramesh Yadav Ruchika Chaturvedi Tanushree Jha Shivangi Baswal Vikas Sachdeva Sanket Kale Pratik Kaushal Shukla Thanks to our amazing partners to bring this alive ! Enormous Creative ashish khazanchi Akriti Sushil Ganju Darsh Parekh SPRIHA WALIA Prashant Godbole

800 Million transactions/day.… I.e. 9,000+ transactions per second… 10 years ago, UPI didn’t exist. Today, it’s how India pays. Let that sink in: • 9,000+ transactions per second • From chai taps to billion-dollar flows • Used by everyone: from street vendors to CXOs But here’s the real magic.. We don’t use UPI anymore. We don’t even notice it. It just… works. Built by NPCI on top of India Stack, UPI turned something complex into something instinctive: 👉 Trust at scale 👉 Real-time money movement 👉 Zero learning curve That’s insanely hard to build. And yet India made it feel effortless 🇮🇳 Want to truly understand why the world is studying UPI? Don’t just look at the numbers. Read this https://lnkd.in/dMk-5wmf #IndiaTech #BuiltInIndia #IndiaInnovation #ProudlyIndian #UPI #DigitalPayments #Fintech #IndiaStack #NPCI

It's been a while since I have stepped into the role of Lead – Enterprise Risk Management at JioBlackRock Investment Advisers Joining the Wealth Management space at JioBlackRock is a unique opportunity to be part of a mission that aims to democratize sophisticated investment solutions for millions of Indians. My focus is on building a resilient and world-class risk framework that supports this vision of financial inclusion and digital-first advisory. I look forward to contributing to an organization that is truly redefining the landscape of Indian wealth management.

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