Tech Supply Chain Solutions

Fruits & vegetables fetch quick commerce platforms 2x margins than packaged foods (only if they solve 1 critical problem) Zepto is investing heavily in cold chain logistics like refrigerated trucks while partnering with the Transport Corporation of India to expand its storage and distribution capabilities in the south. Cold chain logistics isn't new. The USA and Japan have had it since the 1950s, with over 70% coverage of perishable goods like dairy, meat, fruits, and vegetables. But India’s coverage remains just 4%. This is why we lose 15% of our total produce between harvest and consumption, amounting to an estimated economic value of ₹926 billion (USD 11.1 billion). For example, mangoes from Malihabad often lose 30% of their value before reaching Delhi markets just 500km away. The cold chain approach involves maintaining consistent temperatures across the entire journey, from farm sorting to dark store to doorstep delivery. While packaged goods offer 14-15% margins, fresh produce can deliver up to 30%. India's challenge remains substantial. For logistics companies, this creates 3 major opportunities: 📍 Temperature-controlled last-mile delivery networks that can maintain freshness for 10-minute deliveries 📍 Tech-enabled quality monitoring systems that reduce rejections and returns 📍 Specialized warehousing solutions near consumption centers to minimize handling Zepto is already processing 20 lakh+ units of fresh produce daily, but they're not alone in this race: 👉 Blinkit is leveraging Zomato's logistics network for perishables  👉 Instamart is integrating AI-powered cold storage into its micro-warehouses  👉 bigbasket's BB Now is using Tata's supply chain expertise to strengthen their fresh produce operations. The cold chain market in India is projected to grow from $14.5B to $53B by 2032. Early movers will capture the most value. Cold chain masters win on both margins and sustainability, making speed to market the only real question. Are you noticing a difference in produce quality between quick commerce apps? #QuickCommerce #ColdChainLogistics #FreshProduce

ThrillingTechTrends #13 - Carbon Intelligence ♻️ is redefining what’s possible. Measuring freshness is great. But measuring freshness with impact is even greater: Using a combination of real-time IoT sensor data, AI-based route prediction, and lifecycle-based emission models, it’s becoming possible to measure and manage CO₂ output across every leg of the fresh supply chain — from pre-cooling and storage to multimodal transport and last-mile delivery. Here’s what’s happening behind the scenes: ✅ Telematics & Sensor Fusion: Temperature, humidity, energy consumption, and fuel data are aggregated via connected devices on trucks, containers, and warehouses. ✅ Edge Analytics: Emission data is processed on the move to detect anomalies, idle times, and inefficient cooling cycles — enabling instant optimization. ✅ AI-Driven Carbon Forecasting: Predictive models simulate carbon impact under different routing, timing, and packaging scenarios to support low-emission decisions in real time. ✅ Dynamic CO₂ Attribution: Each product unit can be assigned a precise carbon footprint based on actual transport conditions, not static averages — enabling true product-level transparency. The result? A smarter cold chain that keeps food fresh and carbon footprints low. Decarbonizing fresh logistics is no longer an ambition — it’s getting reality & will make our world better. 🌎 #CarbonIntelligence #FreshLogistics #ColdChainTech #SupplyChainInnovation #IoT #SustainableLogistics #AIinLogistics #GreenTech

𝐕𝐏 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Can you integrate with our deployment tracker? 𝐆𝐑𝐂 𝐕𝐞𝐧𝐝𝐨𝐫: We don’t see that tool often, so we haven’t integrated with it yet. 𝐕𝐏 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: So what do I do? 𝐆𝐑𝐂 𝐕𝐞𝐧𝐝𝐨𝐫: Export the data manually and upload it to our platform. This conversation happens every single day. Compliance tools can store evidence, track deadlines, and generate reports. But do the tools actually DO anything? Don’t be fooled. That's not automation. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐟𝐥𝐚𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐞𝐦. So what does an autonomous system look like? 𝟏/ 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 The platform deploys autonomous agents that navigate complex workflows, authenticate into systems, and capture compliance artefacts in real time without engineering intervention. E.g.: Your deployment tracker logs a production release at 2 AM? The agent captures it automatically. Evidence collected, no manual exports. 𝟐/ 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐭𝐞𝐬𝐭𝐢𝐧𝐠 AI can automatically and continuously test controls across systems, detecting control failures or exceptions in near real time. Instead of discovering something during your quarterly audit prep, you know the moment something breaks. E.g.: Access reviews that run continuously, vulnerability management that monitors remediation status daily. 𝟑/ 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐜𝐡𝐚𝐧𝐠𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞 Using Natural Language Processing, AI tools can scan thousands of regulatory websites, government updates, and legal documents daily. E.g.: New GDPR guidance drops? The platform reads it, maps it to your existing controls, and flags gaps. 𝟒/ 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 Agentic AI can autonomously monitor compliance obligations, detect emerging risks, and trigger appropriate workflows without human initiation. E.g.: Build an agent that auto-generates evidence for that spreadsheet. Create a workflow that routes vendor assessments differently based on risk tier. Design a control that adapts to how your multi cloud environment works. This is why the next generation of compliance programs won't be run by teams chasing evidence. They'll be run by autonomous systems that never sleep or wait for a manual export. What's the one manual workaround in your compliance process that you wish would just handle itself?

🌱𝗦𝗲𝗻𝘀𝗼𝗿𝘀 & 𝗜𝗼𝗧 𝗮𝗿𝗲 𝗼𝘂𝗿 𝘂𝗻𝗮𝘃𝗼𝗶𝗱𝗮𝗯𝗹𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 Why? Because sustainable AI-powered agriculture without sensors is... a bit clumsy. Practically impossible. But firstly, let's understand what do I mean by sensors, IoT and AI 𝟭. 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗦𝗲𝗻𝘀𝗼𝗿𝘀? Think of sensors as the farm's nervous system (either indoor or in field - meaning the same). They're clever little devices that measure everything from moisture to light, temperature to soil chemistry. Camera is also a sensor! These digital helpers tell us exactly what's happening 24/7. 𝟮. 𝗪𝗵𝗮𝘁'𝘀 𝗜𝗼𝗧? Internet of Things (IoT) is just a fancy way of saying "connected stuff." Imagine that a sensor "sees" that your tomatoes need irrigation but how it can "speak" to you? IoT is here to help. It brings info to your phone or computer. All in real-time! 𝟯. 𝗪𝗵𝗮𝘁'𝘀 𝗔𝗜? Artificial Intelligence is your super-smart junior agronomist :). (S)he can spot patterns in all that sensor "says" and IoT brought to the device + learns from past week / season/ year. 🌍 Why We Need This Now More Than Ever ↳ 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗖𝗵𝗮𝗻𝗴𝗲 𝗜𝘀 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗥𝗲𝗮𝗹 - Remember when we could predict weather by looking at the sky? Those days are gone!  - Temperatures are going crazy. We need hyperlocal weather prediction just to stay in the game. ↳ 𝗖𝗼𝘀𝘁𝘀 𝗔𝗿𝗲 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁𝗶𝗻𝗴 📈 - Water isn't cheap anymore and won't be. - Agricultural chemicals? I even don't want to speak about the prices... ↳ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗔𝗿𝗲 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗣𝗶𝗰𝗸𝗶𝗲𝗿 (Real talk: I personally love those quirky-looking carrots 🥕 and not-perfectly-sized fruits 🍎 - they taste just as good and help reduce food waste! But...) - Most customers want picture-perfect produce! 🔍 𝗛𝗼𝘄 𝗝𝘂𝘀𝘁 𝗮 𝗙𝗲𝘄 𝗦𝗲𝗻𝘀𝗼𝗿𝘀 𝗔𝗿𝗲 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗚𝗮𝗺𝗲 1️⃣ Visual Sensors (Cameras): - Spot plant diseases / track pest infestations - Monitor crop growth daily - Grade produce automatically 2️⃣ Soil & Water Sensors: - Measure moisture at different depths - Track nutrient levels - Monitor soil health 3️⃣ Environmental Sensors - Track temperature variations (and even can detect frost risk!) - Monitor humidity levels - Measure CO2 levels (if in the greenhouse) Finally, my main message: 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗳𝗮𝗿𝗺𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗳𝗮𝗿𝗺𝗲𝗿𝘀 - 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗴𝗶𝘃𝗶𝗻𝗴 𝘁𝗵𝗲𝗺 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿𝘀! 💪🏼 Sensors, IoT and AI are here to help work smarter not harder. ----- What do you think on the matter? Follow me, Maryna Kuzmenko, Ph.D 🇺🇦 to continue our #AgTech conversation. Next time I'll write about sensors for indoors and in field farming.

I watched a robot deliver food from a restaurant two blocks away. It was ridiculous and SO F**KING COOL! Who is shaping the future of autonomous food delivery? Coco: The new OpenAI partnership and fresh $122M in Series B funding for enhanced path planning lays the foundation for market dominance Manna Air Delivery: 3-minute drone deliveries are proving the speed advantage Wing: Multi-modal partnerships (see: Serve Robotics collab) are expanding their addressable market Nuro: Licensing pivot + deepening relationships with Uber highlights strategic focus to become the foundational autonomous vehicle technology provider Starship Technologies: With 8M+ deliveries; scaling from 50 campuses to 150 cities globally shows sustainable execution Zipline: Remains the drone delivery heavyweight with restaurant partnerships pushing beyond traditional medical deliveries Several key categories define the autonomous food delivery market: → Sidewalk Delivery Robots: Small autonomous robots designed for short-distance deliveries in pedestrian areas → Road-Based Autonomous Vehicles: Larger autonomous delivery vehicles capable of operating on public roads → Hybrid Remote-Operated Systems: Robotics solutions combining autonomous navigation with remote human oversight → Multi-Modal Delivery Platforms: Integrated systems combining various autonomous delivery methods with traditional logistics → Indoor/Controlled Environment Robots: Specialized robots for deliveries within buildings, hospitals, and controlled facilities → Drone Delivery Integration: Aerial autonomous delivery systems for rapid food delivery Market leaders in each category are emerging. But, while the market leaders are gaining commercial traction, winning key partnerships, and attracting funding, several players, including once-promising names are struggling to deliver (pun intended). In a market that once was betting on promise, execution is now table stakes. What recent highlights tell us about the evolution of the market: ↳Market leaders are now making millions of deliveries with 99% autonomy; proving scalability ↳Major platforms (Uber, DoorDash) are all-in with partnerships, driving adoption and revenue to fuel the next wave of innovation ↳Tech advancements and maturation are enabling the market shift from confined, controlled pilots to complex urban deployments ↳Investors are willing to write (big) checks to companies that are proving commercial traction with Nuro, Coco, Manna, and Neolix all raising fresh rounds this year We're witnessing the transition from “oh, look a robot” to "scalable last-mile infrastructure." 2025 is shaping up to be the year your Uber Eats or DoorDash driver isn’t a driver at all. P.S. Want more insights on the companies building the future of food delivery? Comment "insights delivered" below for *free* access to CB Insights' data and insights on the autonomous food delivery markets.

𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈: 𝐓𝐡𝐞 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐞𝐫 What sets agentic AI apart is its ability to act with purpose. It’s not just reacting to input but considering objectives and making choices to achieve them. Building agentic AI systems involves integrating perception, reasoning, and action execution into a single cohesive pipeline. 𝑷𝒖𝒓𝒑𝒐𝒔𝒆 𝒗𝒔. 𝑹𝒆𝒂𝒄𝒕𝒊𝒐𝒏: Traditional AI might work by reacting to inputs: you provide data, and it gives you an answer based on patterns. Agentic AI, on the other hand, builds an understanding of its environment and defines goals in that context. Instead of “if input, then output,” it asks, “what do I need to achieve?” and then figures out the best way to get there. 𝑫𝒚𝒏𝒂𝒎𝒊𝒄 𝑫𝒆𝒄𝒊𝒔𝒊𝒐𝒏-𝑴𝒂𝒌𝒊𝒏𝒈: Consider the autonomous drone example. Its primary objective is to deliver a package. To do that, it must: Perceive its Environment: Gather data from sensors (visual, radar, GPS, etc.). Analyze and Plan: Continuously update its model of the surroundings. It’s not just looking out of the window—a suite of algorithms evaluates obstacles, weather conditions, and unexpected events. 𝑬𝒙𝒆𝒄𝒖𝒕𝒆 𝒘𝒊𝒕𝒉 𝑭𝒍𝒆𝒙𝒊𝒃𝒊𝒍𝒊𝒕𝒚: It selects a route optimized for speed and safety, and adjusts in real time if new obstacles appear. 𝑪𝒐𝒏𝒕𝒊𝒏𝒖𝒐𝒖𝒔 𝑨𝒅𝒂𝒑𝒕𝒂𝒕𝒊𝒐𝒏: The drone isn’t following a static map. Its AI continuously balances current sensor data with pre-defined objectives. That means dynamically recalculating routes, re-prioritizing tasks, and even handling emergencies—all without any human intervention. 𝑽𝒊𝒔𝒖𝒂𝒍𝒊𝒛𝒊𝒏𝒈 𝒕𝒉𝒆 𝑷𝒓𝒐𝒄𝒆𝒔𝒔 Here’s an ASCII flowchart to illustrate how an agentic AI system (like our package-delivery drone) might operate: [Mission Objective] │ ▼ [Gather Environmental Data] │ ▼ [Analyze & Update Situation] │ ▼ [Plan Optimal Route & Evaluate Options] │ ▼ [Execute Movement/Actions] │ ├────► [Monitor Outcomes] │ └────► [Adapt and Re-plan if Needed] Agentic AI’s capacity for purpose-driven action isn’t limited to drones. Think about: 𝑺𝒆𝒍𝒇-𝑫𝒓𝒊𝒗𝒊𝒏𝒈 𝑪𝒂𝒓𝒔: Navigating complex urban landscapes by predicting pedestrian movements and adapting to traffic in real time. 𝑹𝒐𝒃𝒐𝒕𝒊𝒄 𝑨𝒔𝒔𝒊𝒔𝒕𝒂𝒏𝒕𝒔: Working in dynamic environments like hospitals where they must balance multiple tasks simultaneously. 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒊𝒂𝒍 𝑨𝒖𝒕𝒐𝒎𝒂𝒕𝒊𝒐𝒏: Systems that manage entire supply chains, dynamically optimizing routes, resources, and logistics based on current conditions. The promise of agentic AI extends far beyond automation—it’s about infusing systems with a kind of “digital intuition” that enables smarter, safer, and more efficient operations across diverse applications.

75% of cold chain shipments still rely on ice, foam, and decades-old methods. That number isn’t a technology problem. It’s a comfort problem. We know the cold chain is fragile. Every hand-off adds risk. Every delay increases exposure. One miss can turn safe product into waste or worse. The tools already exist. → Real-time temperature tracking. → Predictive alerts. → Automated unloading that cuts exposure from 30 minutes to under four. → Reusable containers that actively control temperature instead of hoping ice holds. So why hasn’t the industry moved? Because legacy systems feel safe. Because visibility forces accountability. Because real-time data exposes weak processes fast. Ice doesn’t raise alerts. Data does. Modern cold chain systems don’t just protect temperature. They show where decisions break down. Where hand-offs fail. Where teams need better training and support. That’s uncomfortable. But it’s cheaper than waste. Cheaper than recalls. Cheaper than lost trust. Cold chain isn’t storage anymore. It’s orchestration. And the next leaders won’t be the ones defending old methods. They’ll be the ones willing to modernize how people, systems, and accountability work together.

Agentic AI can save companies 1000+ hours preparing for and maintaining compliance programs across frameworks like SOC 2, ISO 27001, PCI, etc. Here’s a behind-the-scenes look at how we’re building in public. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 𝗔𝘂𝗱𝗶𝘁 𝗣𝗿𝗲𝗽 GRC teams spend hundreds (sometimes thousands) of hours chasing evidence, validating controls, managing findings, and reporting status. It’s a fragmented process: → Evidence owners don’t know what to upload → Analysts manually review every file → Findings get buried in spreadsheets → Executives ask for status updates that take days to compile The process is inefficient and it’s risky. Gaps get missed. Deadlines slip. Teams burn out. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 We’re building a suite of agents that work together to automate the most painful parts of compliance: → 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗚𝗮𝘁𝗵𝗲𝗿𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 Slack-integrated assistant that answers “what do I need to upload?” and references prior submissions and validates evidence before analyst review. → 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 Reviews evidence against control requirements and flags gaps and auto-identifies findings. → 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗔𝗴𝗲𝗻𝘁 Turns findings into risks and remediation tasks and links everything back to controls for traceability. → 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 𝗦𝘁𝗮𝘁𝘂𝘀 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 Aggregates data across controls, risks, and tasks and generates executive-ready summaries with narrative and metrics. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: 𝟭𝟬𝟬𝟬+ 𝗵𝗼𝘂𝗿𝘀 𝘀𝗮𝘃𝗲𝗱 We estimate these agents will save GRC teams over 1000 hours annually. That’s time they can spend on strategy, not spreadsheets. It also means faster audits, fewer gaps, and better visibility for leadership. 𝗪𝗔𝗡𝗧 𝗧𝗢 𝗦𝗘𝗘 𝗧𝗛𝗜𝗦 𝗜𝗡 𝗔𝗖𝗧𝗜𝗢𝗡? If you want to see some of this in action, we will be previewing part of the solution next Thursday 9/4. Link in the comments. 👇

67% of security teams still run compliance audits manually in 2026. That is not a resource problem. That is a $2.1M mistake waiting to happen. AI reduces audit prep time by 40 to 60%. Yet most compliance teams only touch it during audit season. Then wonder why they are always scrambling. Here is the full masterclass on using AI for compliance the right way. 3 modes. Most teams only know one. Assist Ask questions. Draft policies. Find gaps faster. This is where most teams stop. It is also the least powerful mode. Automate AI reads your documents, maps controls, flags gaps, and creates audit-ready reports automatically. No manual pulling. No last-minute panic. Orchestrate This is where compliance becomes operational. Run tasks automatically. Collect evidence. Score risks. Generate reports on schedule. Your compliance posture is always visible. Always current. The 5-step workflow no one talks about: Step 1 : Start with gap analysis Upload your policies and security controls. Ask AI to map them against your target framework. Get a prioritized gap list in minutes, not weeks. Step 2 : Connect your evidence sources Link Jira, ServiceNow, AWS Config, Azure Policy, Google Workspace. AI pulls evidence automatically and tags it to the right control. Step 3 : Build custom compliance skills Run an audit workflow once manually. Then tell AI to package it into a reusable template. It captures the steps, evidence sources, and reporting format automatically. Step 4 : Automate reporting Schedule daily risk scores, weekly control coverage reports, and monthly board-ready summaries. No manual updates. No version confusion. Step 5 : Move to continuous compliance Framework coverage tracked live in the background. No more point-in-time audits. No more last-minute scrambles before an assessor walks in. The 3 mistakes killing compliance programs: Mistake 1 : Using AI only at audit time AI used only during audit season is a last-minute patch. Embed it in your daily workflow. Continuous compliance beats reactive compliance every time. Mistake 2 : No framework context or memory AI gets smarter when you tell it your frameworks, risk appetite, and compliance history. Set your instructions once. It works with that context in every session. Mistake 3 : Not connecting your evidence sources AI without your actual data is just a policy writer. Connect your cloud environments, ITSM tools, and asset management systems. That is where the real compliance power starts. The teams winning in 2026 are not working harder. They built a system that works while they sleep. Continuous monitoring. Automated reporting. Live framework coverage. The audit does not surprise them. They are always ready. Compliance is not a once-a-year event. It is an always-on operation. Which of the 3 mistakes is your team still making? ♻️ Save this and repost it for your compliance team.

Years ago, the idea that you could place a tiny sensor on a palm tree and detect pest before visible damage appeared would have sounded ambitious. Maybe even unrealistic. A device small enough to hold between two fingers can listen for activity inside the tree, collect sound patterns, and help identify a problem that would normally stay hidden until it is too late. The #redpalmweevil is difficult to manage not only because it damages trees, but because the early signs are almost invisible from the outside. By the time people notice that something is wrong, the tree 🌴 may already be seriously affected. Can we know earlier? Can we act before the damage spreads? Can a small device give us a signal that the human eye cannot see? —- 👇🏼 So in this case, the real challenge is not only the hardware, the battery life, the connectivity, or the data transmission. The real challenge is timing. This is the kind of IoT that I believe has real value. Not technology for the sake of technology, but technology that helps people make better decisions earlier. For agriculture, cities, landscapes, and infrastructure, these small devices are quietly changing how we monitor the world around us. Not by replacing people, but by giving them information they could not easily access before. #IoT #SmartAgriculture #LoRaWAN #SensorTechnology #AgriTech #SmartCities #IndustrialIoT