Litmus ads

Litmus is finally a two-product company: Litmus Edge and Litmus Data Catalog. Both are stepping stones toward Industrial AI and autonomous operations. A bit of backstory. Three years ago, Litmus Data Catalog was a vision, but it wasn't on a roadmap. With everyone chasing AI and AI agents, the obvious choice would have been to launch one more AI service. Instead, we worked backwards from what's actually needed to make AI agents useful, trustworthy and robust on the plant floor. We have more data access on the plant floor than any other vendor in the market. If we understand what that data means, we can introduce an intelligent layer that drives operational efficiency at a level no one has seen before. I think the bigger shift is this: one plant that works isn't an architecture. If every site has to rediscover what its data means, every AI use case starts over at the next plant. When the context travels with the data, something that works in one plant can be repeated in the next. This is a proud moment for all of us at Litmus. Thank you to the customers who gave us feedback and pulled us out of private preview. Learn more: https://lnkd.in/eBWaBJ-T

This folded-up page has been sitting on my desk for the last few weeks. It’s the first napkin idea John and I had when we were talking about what we wanted Litmus to be. What's interesting isn't what's written on it. It's what's missing. There's not a single mention of AI. No LLMs. No agents. No copilots. Back then, we were obsessed with a simpler problem: Why was it so hard to make industrial data usable? Looking back, I think we were solving the problems AI would eventually run into years before AI showed up. We just didn't know it. People ask me when Litmus became an AI company. I don't think we ever did. I think AI finally caught up to the problem we'd been working on all along. This page reminds me that the goal isn't to chase whatever technology everyone is talking about today. It's to solve problems that will still matter when the next wave arrives. That's been true for Litmus for the last decade, and it's still shaping what we build today. And yes—we're still making it AWESOME. Learn more: https://lnkd.in/gZcgxU9Q

We did not pivot to AI. We planned for it. When we started Litmus in 2014, the industry was still arguing about whether to send factory data to the cloud. We were convinced the harder question would come later: once everyone had the data, would any of it be usable across a network of plants that were never built to match? That is the question every manufacturer is now facing. The AI is not the constraint. The data underneath it is. Litmus has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms. Being named a Challenger last year and a Niche Player before that, we believe this progression reflects the same journey our customers have been on. Thank you to the teams who have been solving this with us for over a decade. Learn more: https://lnkd.in/eF2_bB4w

Sticky green stuff on your bike is never good. The other day I heard a sound followed by spray covering my legs. I was doing one my usual morning bike rides when one of tires was cut by something. The green stuff is sealant liquid inside the tire which was supposed to plug the hole but it failed. That was my third flat with complete failure in July. Same model. Bought at my usual bike supply shop. Bad luck? I don't think so. Two other friends rode with a new set of identical tires and also flatted. Over the past 8 years, I had only one flat. Three completely damaged tires in one month is simply not normal. I assume it must be a quality issue. Depending on the product you are selling, quality is nothing to get wrong. Industries like pharma or F&B simply can't compromise. Safety is key. And problems can quickly get very expensive when you have to throw stuff away, too. That is why a lot of our customers leverage data to spot quality problems early on. When a quality issue hits, the first question must be: what caused it? Answering that requires tracing a defect back to the asset, line, batch, lot, and operating conditions at the exact moment it occurred. Most manufacturers can't do that today. Litmus provides the data foundation that makes full traceability possible, from edge to enterprise. And what about my tires? No more cost experiments. I went with an alternative from an Italian competitor. The German manufacturer lost a loyal customer. Want to learn more: https://lnkd.in/dY9_Knnn

Everyone loves a shortcut (....so do I...) The Tour de France just finished and everyone is talking about Pogacar's 'secret': lactate gels. His sponsor apparently developed a new kind of gel with exogenous lactate that can provide a performance boost towards the end of a stage. A consumer version should be available shortly. I assume it will be very expensive. Looking at the science, it seems that this is a tool that only works for athletes performing at the absolute highest level. Most amateurs will only feel an effect in their wallet. And here is where the breakdown happens: People often focus on the tip of the iceberg but nobody talks about the real 'secret': thousands of hours of unglamorous and brutal training leading up to the Tour de France. This pattern repeats everywhere. People and organizations reach for quick fixes and hope to avoid doing the hard work. CEOs ask for wellbeing websites to fix a deteriorating culture. Share buybacks to replace a solid growth strategy. Supplements to help with sleep while drinking alcohol before bed. It's optimizing the top 5% and neglecting the 95% foundation . The same thing is happening with industrial AI. Executives want it. Investors demand it. Teams then scramble to deploy the first AI project. Flat files and fragile pipelines are supposed to deliver critical data. Costs spiral and pilots hardly ever scale beyond one plant. AI is the 5%. Data infrastructure is the critical backbone. Our CEO Vatsal Shah said it plainly in a recent The IT/OT Insider episode: skip the foundation and the agents don't work. Scalable industrial AI only works when it sits on clean, reliable, real-time data. It includes machine connectivity and contextualized data. Edge infrastructure that doesn't fall over when the internet goes down. That's the 95% that makes a real and lasting difference. It's not as daunting as it sounds. Many Litmus customers roll this out across 30–60 production sites in 12 months. Build the base. Then win the race.

AI is cool and all - but have you ever integrated data from a chart recorder? 🤩 This beauty (a Yokogawa HR2400) arrived in our lab this morning and instantly caught my eye. It's not a museum piece - it's actively used by customers, capturing critical operational data. It's the perfect reminder that a true digital transformation doesn't mean "rip and replace." It means integrating everything. When we say "Scale = Litmus" we mean total operational context. Scale = Integrating all sites. Scale = Enabling as many usecases as business needs. Scale = Integrating all data, from a 20-year-old recorder to a brand-new sensor. 🚨 Stop falling for the "best-of-breed" trap - where every vendor solves only a small piece of the puzzle. You can’t buy one vendor for OPC UA, another for MTConnect and then write custom code for chart recorders. And then buying 2 more to do DataOps. Context from this chart recorder matters. Context from every legacy system matters. Scale means a unified, enterprise-wide data strategy - and that’s what we’re enabling every day at Litmus. Check it out: https://lnkd.in/gjtBWVgM