Tech Stack Management

𝗧𝗵𝗲 𝗢𝗦𝗜 𝗺𝗼𝗱𝗲𝗹 𝗹𝗼𝗼𝗸𝘀 𝘀𝗶𝗺𝗽𝗹𝗲 — 𝘂𝗻𝘁𝗶𝗹 𝘆𝗼𝘂 𝗺𝗮𝗽 𝗢𝗧 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝘁𝗼 𝗶𝘁. In IT, most communication neatly follows familiar stacks: HTTP, DNS, TLS, TCP/IP, Ethernet. In OT, the picture is different. Some protocols sit cleanly at the application layer. Some bypass normal TCP/IP behavior. Some are tightly coupled to Layer 2 timing. Some still live close to raw physical signaling. That is why 𝗢𝗧 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 cannot be designed only with traditional IT assumptions. A few practical reminders: 𝟭. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻-𝗹𝗮𝘆𝗲𝗿 𝗢𝗧 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝘀𝘁 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝗺𝗲𝗮𝗻𝗶𝗻𝗴. Modbus, DNP3, IEC 60870-5-104, OPC UA, BACnet/IP and IEC 61850 MMS define what the data means — not just how it moves. 𝟮. 𝗟𝗮𝘆𝗲𝗿 𝟮 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗶𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗶𝗻 𝗺𝗮𝗻𝘆 𝗢𝗧 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀. GOOSE, Sampled Values, EtherCAT and PROFINET RT/IRT may require special visibility and segmentation approaches. 𝟯. 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝘀𝗲𝗿𝗶𝗮𝗹 𝗮𝗰𝗰𝗲𝘀𝘀 𝘀𝘁𝗶𝗹𝗹 𝗺𝗮𝘁𝘁𝗲𝗿. RS-232, RS-485, HART and 4–20 mA loops may not look “cyber” at first, but they can directly influence process behavior. 𝟰. 𝗢𝗦𝗜 𝗶𝘀 𝗮 𝗺𝗼𝗱𝗲𝗹, 𝗻𝗼𝘁 𝗮 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝗺𝗮𝗽. Real industrial stacks collapse layers, tunnel protocols, and behave differently from enterprise applications. For OT security, the real question is not only: “𝗪𝗵𝗮𝘁 𝗽𝗼𝗿𝘁 𝗶𝘀 𝗼𝗽𝗲𝗻?” It is: 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗶𝘀 𝘀𝗽𝗲𝗮𝗸𝗶𝗻𝗴, 𝘄𝗵𝗮𝘁 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝘀𝗲𝗻𝘁, 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗰𝗼𝗻𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗰𝗮𝗻 𝗶𝘁 𝗰𝗿𝗲𝗮𝘁𝗲? #OTSecurity #ICSSecurity #IndustrialCybersecurity #IEC62443 #SCADA #CriticalInfrastructure #CyberSecurity #OperationalTechnology

You didn't build a 'modern data warehouse.' You built the world's most expensive junkyard. I recently audited a client's infrastructure with 120+ tables loading every night. -Not one person could explain what half of them were for. -Dashboards contradicted each other. -Analysts were burning hours tracing dependencies instead of building anything useful. -If few engineers left everything would crumble. On the top the cloud bill and data volume is growing. Here’s the problem no one wants to admit Your ELT isn’t broken. It’s bloated. We’ve confused “accessibility” with “intent” Tools made it easy to load everything so we did. And over time, your data warehouse turned into a junkyard. The Hidden Cost of Loading Everything Every table you load has a cost: Storage & compute. (That Snowflake bill didn’t double by accident.) Engineering time. (Maintaining pipelines no one uses.) Trust. (Conflicting numbers, different definitions, zero confidence.) Everything you add to tech stack can and most likely will become a liability faster then the asset. The result? You’re sitting on a goldmine of data but its usless Creating 1. Start with Decisions, Not Sources Every dataset should answer a business question. If you can’t tie it to a KPI, a metric, or a decision it doesn’t deserve a pipeline. Rule: “If we can’t name who uses it or what decision it drives, delete it.” 2. Audit Everything You Load Run a two-hour audit of your pipelines. Tag each as: Must-Have: Directly drives a core KPI or compliance requirement. Nice-to-Have: Useful but infrequently used. Archive: Unused in 90+ days. When we did this for a Healthech and finance client, 40% of their pipelines had no active usage. They were maitaining duplicate pipelines, cost a fortune but nobody haven't even looked at 3. Connect Every Pipeline to the P&L Nobody cares many pipelines you’ve built they care how it impacts margin. Every data initiative should tie to one of three levers: Cost reduction (cloud spend, engineering time) Revenue enablement (forecast accuracy, churn prevention) Risk reduction (audit accuracy, compliance) If it doesn’t hit one of these? It’s noise 4. Assign Ownership The most expensive part of pipeline and ETLs isn’t compute. It’s ambiguity. No one owns the data. No one knows who built it. No one knows what engineer responsible for it. Assign a data steward per domain responsible for purpose, lineage, and consumers. Accountability drives cleanup faster than any governance tool. 5. Enforce an “ROI Gate” Before Every New Ingestion Before any new source is added, ask: “What decision does this support, and what’s the expected ROI?” You’ll kill 50% of waste before it even starts. Every unnecessary dataset or tools you load burns margin, trust, time and complexity. Most likely a liability. Build lean, trusted, scalable and AI-ready data architecture. Stop loading everything. Start loading with intent.

Most sales orgs have too many tools and not enough results. Ya'll are spending money in the wrong places. You've got a CRM. You've got a dialer. You've got an SEP. You've got conversation intelligence. You've got 14 dashboards nobody looks at. And your reps are still manually researching prospects. Still sending the same generic sequences. Still doing demos that don't convert. The stack is bloated with the WRONG things. The results are flat. Meanwhile, the modern buyer has changed. They want to self-educate. They want data. They want to move on their timeline, not yours. Or your still using what was cool 2-5 years ago because it was 'on the quadrant' If your tech stack isn't built around AI, data-driven decisions, and meeting buyers where they are — you're already behind. Tech has changed. There are some new up and comers that are just rocking right now 𝗧𝗛𝗘 𝗖𝗔𝗧𝗘𝗚𝗢𝗥𝗜𝗘𝗦 𝗧𝗛𝗔𝗧 𝗔𝗖𝗧𝗨𝗔𝗟𝗟𝗬 𝗠𝗢𝗩𝗘 𝗧𝗛𝗘 𝗡𝗘𝗘𝗗𝗟𝗘 These aren't tools I just talk about. I actually use them with my team. One of the few "influencers" left that's still in the trenches building. I get asked all the time my 'stack' so kicking the year off with some of my favorites. Here's where I'm focused for 2026: 𝗗𝗮𝘁𝗮 & 𝗘𝗻𝗿𝗶𝗰𝗵𝗺𝗲𝗻𝘁 Bad data = wasted activity. Period. If your reps are calling wrong numbers and emailing dead addresses, no amount of "more dials" fixes that. Using: Cargo, ZoomInfo, TitanX 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 The manual stuff that eats your team's time — follow-ups, research triggers, multi-touch sequences — needs to run without humans babysitting it. Using: Swan AI, Trigify 𝗗𝗲𝗺𝗼 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 Your AEs are doing the same demo 47 times a month. Most of those demos are qualification calls in disguise. What if prospects could experience the product on their own time? 24/7. Customized to their use case. Scalable. Repeatable best practices baked in. Better qualified prospects. Shorter cycles. Higher conversion. Using: Consensus 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 "Find me companies that just raised a Series B, are hiring SDRs, and use Salesforce." That level of specificity used to take hours. Now it takes seconds. Using: Exa 𝗧𝗛𝗘 𝗕𝗜𝗚𝗚𝗘𝗥 𝗣𝗢𝗜𝗡𝗧 Stop buying tools because they're "cool" or because your competitor has them. Buy tools that solve a specific constraint in your funnel. If connect rate is killing you — fix the data. If cycle time is too long — fix the demo process. If reps are drowning in admin — fix the workflows. Constraint first. Tool second. The orgs that win in 2026 will be the ones using AI to make smarter decisions, move faster, and meet buyers where they actually are. Most orgs have it backwards. They buy the tool and then try to find a problem for it to solve. That's how you end up with 47 logins and the same results. Be intentional with your stack, ya'll.

Most Industrial IoT projects fail for one simple reason - companies focus on devices first, architecture later. But successful IIoT systems are built as layered data and intelligence platforms, where information flows from machines all the way to enterprise decisions. Industrial IoT architecture connects physical machines, networks, data platforms, and AI systems into a unified operational stack. Each layer plays a specific role — from collecting machine signals to generating predictive insights that optimize operations. Here’s how a typical Industrial IoT architecture stack is structured 👇 ➞ Device Layer (Industrial Assets & Sensors) Machines, PLCs, robots, and sensors generate real-time operational data from physical environments. ➞ Connectivity Layer (Industrial Networks) Protocols and networks like MQTT, OPC-UA, Modbus, Ethernet, and 5G move device data across systems securely. ➞ Edge Layer (Local Intelligence) Edge computing processes data close to machines for faster responses and lower latency decisions. ➞ Ingestion Layer (Data Collection Systems) Streams and pipelines collect high-volume industrial data and standardize it across systems. ➞ Data Platform Layer (Storage & Processing) Stores time-series machine data and enables batch and real-time processing for analytics. ➞ Application Layer (Industrial Operations) Operational platforms like MES, SCADA, and ERP enable monitoring, automation, and workflow management. ➞ Analytics & AI Layer (Industrial Intelligence) Machine learning models detect anomalies, predict failures, and optimize production systems. ➞ Security & Governance Layer (Cross-Layer Control) Ensures identity management, encryption, compliance, and protection across all industrial systems. Industrial IoT works best when data flows seamlessly from machines → platforms → intelligence → operational decisions. 🔁 Repost if you’re building intelligent industrial systems. ➕ Follow Nick Tudor for more insights on AI + IoT systems that actually ship.

Building your finance tech stack? Here’s a major mistake I see finance leaders make: It’s not overspending. It’s not picking the wrong vendor. It’s something far more fundamental. As monday.com’s CFO, I evaluate all large software purchases - and I’ve noticed a consistent pattern: Many teams obsess over features and discounts, but ignore the 6 factors that actually determine long-term success. Here’s what actually matters at $1B+ ARR and beyond: 👇 1. Don’t Evaluate the Tool in Isolation – But Within the Ecosystem For large purchases, you want to see the full picture - not just the tool itself. Ask: How will this new platform integrate with your existing ones? Most teams evaluate tools in isolation, but real value comes from integrations. Silos destroy efficiency and visibility. The question isn't "Is this the best tool?" but "Is this the best tool FOR OUR ECOSYSTEM?" 2. Benchmark Against Companies at Your Scale It’s a yellow flag if other enterprise organizations aren't using a tool we're considering. When evaluating NetSuite as our ERP, we did research on $500M+ ARR companies to understand their implementation challenges. When we chose Zip for procurement, we looked at companies with similar global reach. The tools that work at $100M ARR don’t necessarily work at $1B+. 3. Assess Implementation Complexity Realistically I am not a fan of solutions that require massive teams just to babysit them. User-friendly and quick internal adoption wins over heavy customization every time. Avoid tools that “promise everything” but deliver nothing for 12+ months. 4. Test for Scalability Early Most finance teams discover scalability issues after it’s too late. Ask: Can the tool scale with us from 100 to 1,000 users without breaking?  We are building our tech stack with enterprise-grade solutions because they grow with us. 5. Strategic Consolidation Beats Best-of-Breed Fewer vendors means better negotiating leverage, simpler operations, and cleaner data flows. At monday, we're ruthless about removing fragmentation that isn’t necessary. 6. Keep Your Stack Evolving with an AI-First Mindset Our finance tech stack is not static. We're constantly updating with a focus on AI capabilities. I suggest you do the same. Every finance leader should reevaluate their tech stack through an AI lens today. *** In summary: The most expensive procurement mistake isn’t overpaying. It’s buying tools that:  1. Can’t grow with you 2. Create siloed data environments 3. Lack AI capabilities in this new AI-first era This mindset has helped us scale efficiently - and avoid million-dollar mistakes.   What’s one finance tool you regret, or swear by? Drop it below👇

Buying Clay won’t get you more leads. Buying Gong won’t make your sales team better on calls. Just like: Buying a set of Wüsthofs won’t make you a better chef. Buying that new Titleist driver? Yeah… it’s not going to magically straighten your slice. Too often we buy tools hoping they’ll solve our problems. But tools don’t solve problems. Processes do. And the best Revenue and Rev Ops leaders I know all follow a playbook when it comes to tooling: 1. Start with the problem, not the tool You need a list—not of tools you want to try, but of business problems you need to solve. Some common ones I hear: "We need to improve our pipeline conversion rate" "We need better forecasting data" "We need to stay in closer touch with customers post-sale" Then you can go hunting for tools that solve those problems. But if you’re just chasing every shiny new AI-powered tool? You’re going to waste time, budget, and team attention. Trust me, the 100th AI SDR tool still sounds pretty cool but it might not be what you need for your business at the current time. 2. Use a structured, data-driven evaluation process “I can see us using this” is not a business case. You need a scorecard. How easy is it to implement? How hard will it be to drive adoption? What’s the expected ROI? Does it integrate with our current workflow and tech stack? The best teams run their tooling like procurement pros. Gut feel isn’t enough, especially when budgets are tight and the stakes are high. 3. No process = no payoff Let’s say you buy the tool. Now what? Without enablement, accountability, and integration into daily workflows, that tool is going to sit on the shelf (just like that $500 driver in your garage). At minimum, you need: -Training plans -Change management -Clear documentation -Leadership support -An incentive or consequence to drive usage If you don’t have a process to make the tool work, you’ve bought shelfware. 4. Continuously re-evaluate your stack We’re in an era where AI is creating entirely new categories almost overnight. Point solutions are becoming features. New platforms are emerging weekly. And you can’t afford to run the same stack just because it worked last year. Great revenue leaders are constantly pruning and optimizing, aligning tools with the evolving needs of the team and the business. The bottom line is software doesn’t make you better. Process does. So before you pull the trigger on the next tool, ask yourself: “Do we have the infrastructure, alignment, and plan to make this successful?” Because trust me, your new Titleist is still going to slice 20 yards right unless you’ve put in the reps (or booked some lessons).

𝗧𝗵𝗲 𝗜𝗜𝗼𝗧 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸: 𝗔𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗟𝗲𝗻𝘀 𝗼𝗳 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 Standards are the foundational "language rules" of #IIoT. While classic #Fieldbus and supervisory protocols have historically facilitated communication at the device and plant levels, newer standards bridge interactions with #cloud-based business systems. 𝗠𝗤𝗧𝗧 𝗮𝗻𝗱 𝗦𝗽𝗮𝗿𝗸𝗽𝗹𝘂𝗴 𝗕: 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 The lightweight #MQTT protocol, originally conceived for bandwidth-limited and unstable network conditions, has become a go-to solution for IIoT connectivity. It uses a Pub/Sub model that only sends data during event changes, reducing network congestion and cutting data transfer costs. Its strong quality-of-service (QoS) levels ensure message delivery in harsh network conditions, an ideal feature for industrial environments. #SparkplugB builds on MQTT, introducing consistent data structures and payloads that allow for real-time data monitoring and device tracking. Its hierarchical topic namespaces improve data organization, facilitating data management across several industrial systems. 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀: 𝗠𝗼𝘃𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗣𝘂𝗿𝗱𝘂𝗲 𝗠𝗼𝗱𝗲𝗹 The layered Purdue model, which is traditionally used in industrial systems, finds challenges when adapting to the volume, variety, and velocity of Industrial Internet of Things (IIoT) data. New architectures are emerging to address these limitations: ▪ 𝗛𝘂𝗯-𝗮𝗻𝗱-𝗦𝗽𝗼𝗸𝗲: This model centralizes data publication through hubs, such as MQTT brokers, before distributing it to multiple applications, consolidating data, and enriching it with contextual metadata. Multiple consumers can access it without overwhelming individual systems. ▪ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗺𝗲𝘀𝗽𝗮𝗰𝗲 (𝗨𝗡𝗦): #UNS is structured through hierarchical topic organization, organizing access to IIoT data. This approach is based on standards like #ISA-95, logically categorizing data to simplify its discovery and usability. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗗𝗮𝘁𝗮𝗢𝗽𝘀 𝗮𝗻𝗱 𝗔𝗜 #DataOps is a discipline that promotes a data-centric culture, breaking down #IT and #OT silos, establishing data governance frameworks for clear data ownership and access, ensuring accessibility, consistency, and usability, and aligning business and technical teams with data-driven objectives. Through data contextualization, where data is tailored to specific use cases, #AI improves data quality, automates system data mapping, and turns it into actionable intelligence. Source: https://t.ly/VPT9C ***** ▪ Follow me and ring the 🔔 to stay current on #IndustrialAutomation, #IndustrialSoftware, #SmartManufacturing, and #Industry40 Tech Trends & Market Insights!

If I were launching a B2B SaaS tomorrow, this is the exact stack I'd use until $30M ARR. After testing 200+ GTM tools and overseeing dozens of GTM stacks, I've got a clear perspective on what drives results and what creates unnecessary complexity. The reality is that success isn't determined by a single tool. It's about creating the right combination of tools, aligning them to your processes, and identifying hidden gaps or redundancies within the stack. That's often where the greatest opportunities exist to lower costs, improve efficiency, and drive better outcomes. Here's the tech stack I'd go with for a PLG B2B SaaS. 1️⃣ CRM → HubSpot Would be my operational centre. I'd start with Sales Hub (90% off in year 1), add Marketing Hub around $1M ARR, and Data Hub later. 2️⃣ Database → Apollo.io Would be my go-to database for sourcing companies and contacts, as it's currently the best combination of data quality, coverage, and pricing. 3️⃣ Email Outreach → Instantly.ai For volume, micro and signal-based email outbound campaigns 4️⃣ LinkedIn Outreach → HeyReach For Tier 1 accounts. 5️⃣ Attribution → Fibbler I'd introduce LinkedIn Ads after PMF, so attribution becomes important. 6️⃣ Data Enrichment → Clay Would use it from day 1 for enrichment, qualification, signals and outbound workflows. 7️⃣ Customer Support → Fin Ai I'd automate as much support as possible from the start. 8️⃣ Payments → Stripe Simple, reliable, and trustworthy. 9️⃣ Website Deanonymisation → Warmly, Τo understand my traffic and also retarget them through outbound. After 1k website visitors. 🔟 Product Analytics → Amplitude After the first 200-300 signups. 1️⃣1️⃣ Product Data Sync → Polytomic To sync usage data directly into HubSpot. 1️⃣2️⃣ Design → Figma For landing pages, ads, decks. 1️⃣3️⃣ Newsletter → beehiiv To nurture my audience. Wouldn't be my first priority. 1️⃣4️⃣ SEO → Semrush After PMF. 1️⃣5️⃣ Workflow Automation → n8n To connect the entire stack and automate repetitive workflows. 1️⃣6️⃣ Partnerships → PartnerStack After PMF. 1️⃣7️⃣ Content & Research → Claude Code Research, content, workflows, analysis. If I was running a Sales-Led motion, I'd also add: • Scheduling & Routing → Chili Piper (after I have 20 people on my sales team, until then, I'd just use Cal.com and HubSpot routing) • Cold Calling → Nooks • Proposals → Qwilr • Conversation Intelligence → Ergo Great companies aren't built by collecting software. They're built by combining the right systems, data, and execution at the right time. Follow Marios Charalampous for weekly GTM tips and insights

When your CRM becomes the linchpin of your entire tech stack, it’s like building a Jenga tower on a single block—it’s only a matter of time before it all comes tumbling down.  Ever had that moment of dread when one CRM update sends ripples through your entire tech stack, causing chaos in Marketing, Sales, and Support? 🫠 The problem lies in over-reliance on a single tool to manage every aspect, turning minor issues into major disruptions. The negative impact of CRM over reliance is clear: ❌ Major Data Silo: Information is trapped within the CRM, making cross-functional collaboration a nightmare. ❌ Scalability Issues: As your business grows, so does the tech debt, making future updates & integrations more complex and costly. So, what’s the solution?  ⚙️ Architect a Distributed Tech Ecosystem: Design your tech stack with specialized tools for different functions. Your CRM should be one of many interconnected tools, not the central hub for everything. Understand that your CRM isn’t a data warehouse or a CDP, so dont architect your system to treat it as such. ⚙️ Implement Data Flow Strategies: Integrate a customer data platform (CDP) to establish a single, unified customer view, and/or use a reverse ETL tool like Hightouch with a data warehouse to distribute that single source of truth data across your tech stack. This ensures your data is not only organized but also activated in a way that supports GTM Strategies. ⚙️ Focus on System Orchestration: Build your tech stack with integration platforms (like Workato, Tray, Cargo, Zapier, Make) to help ensure data flow and interoperability between systems, reducing friction and enhancing efficiency. ⚙️ Design for Modularity and Scalability: Choose scalable, modular solutions for business functions that can evolve as your organization grows, ensuring that your tech stack remains agile and adaptable & you arent over engineering your crm to do things it was never meant to do.  Don’t let your CRM tower wobble—build a tech stack that stands strong! 💪 #RevOps #TechStack #CRM #BusinessGrowth #Integration #Efficiency #Scalability #DigitalTransformation

Bridge X – The Missing Link Between LoRaWAN and Industrial Automation In the world of industrial automation, access to reliable sensor data is everything. Yet, integrating LoRaWAN® sensors into existing PLC and SCADA systems has often been a complex journey – requiring network operators, third-party servers, multiple software layers, and a deep understanding of payload decoding. That changes today with Bridge X. What is Bridge X? Bridge X is a universal LoRaWAN-to-Industrial gateway that makes wireless sensor data available over ModbusTCP and MQTT – the two protocols most widely used in automation and IT systems. Unlike traditional setups, Bridge X is not just a protocol converter. It is a complete LoRaWAN solution in one device, running on a robust DIN rail industrial computer. Key Features That Set It Apart ✅ Built-in LoRaWAN Network Server – Add multiple LoRaWAN gateways and manage thousands of sensors without needing a separate operator or server. ✅ Multibrand Sensor Support – Connect and manage 1,000+ LoRaWAN devices of your choice. ✅ Preloaded Payload Decoders – Over 500+ decoders ready-to-use, with the flexibility to add your own. ✅ Intuitive User Interface – Quickly assign Modbus registers and MQTT topics, eliminating complexity for automation engineers. ✅ Industrial Integration Ready – Seamlessly feeds LoRaWAN sensor values into PLCs, SCADA, and IT platforms. Why Bridge X is a Game-Changer With Bridge X, automation technicians no longer need to rely on external LoRaWAN operators or complicated network/application servers. Everything is consolidated in one platform, giving direct, easy, and scalable access to LoRaWAN sensor data. This versatility makes Bridge X unique in the market. It replaces entire infrastructure stacks while remaining flexible and easy to operate. Whether you’re monitoring energy usage, optimizing HVAC, tracking environmental conditions, or automating critical processes – Bridge X provides the data backbone you can rely on. The Future of LoRaWAN in Automation Bridge X is not just another gateway – it’s the bridge that finally connects wireless IoT sensing with industrial automation in a simple, standardized way. If you are working with PLCs, SCADA, or industrial IT and want effortless access to LoRaWAN sensor values, you will not find a more versatile and powerful solution than Bridge X. #lorawan #modbus #MQTT #BMS Nodeledge ab