4 Things Industry 4.0 02/23/2026

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Happy February 23rd, Industry 4.0!

If you spent last week anywhere near downtown Dallas, you probably noticed a few things: the Hyatt Regency was packed, Reunion Tower had a longer wait than usual, and an unusual number of people were walking around talking about Unified Namespaces at Medieval Times.

ProveIt! 2026 brought over 1,000 manufacturing professionals together for the conference where vendors can't hide behind slide decks. CESMII dropped a potentially game-changing open API, Arlen Nipper (the co-inventor of MQTT) received a well-deserved lifetime award, and the gap between "proven" and "promising" got very visible, very fast.

But while Dallas was busy proving what works, the rest of the tech world was busy proving what doesn't.

Amazon's own AI coding tool decided the best way to fix a minor bug was to delete an entire production environment and start over. Thirteen hours of downtime later, Amazon called it "user error." Sure.

Meanwhile, Cloudflare had its own automation adventure — a routine cleanup script with a buggy API query accidentally yanked 25% of their customers' IP routes off the internet for six hours. Not malicious. Not sophisticated. Just a script that returned everything when it should have returned nothing.

The theme this week? Trust, but verify. Automate, but guardrail. And when someone tells you their tech works — make them prove it.

Here's what caught our attention:

ProveIt! 2026 Wrap-Up: 1,000+ People Showed Up to Watch Vendors Put Their Money Where Their Mouth Is

Most industry conferences follow a familiar script. Vendors rent a booth, hang a banner, fire up a slide deck, and tell you how their solution is going to "transform your operations." You nod politely, grab a pen and a stress ball, and move on to the next booth.

ProveIt! is not that conference.

The details:

ProveIt! 2026 ran February 16–20 at the Hyatt Regency Dallas, and the growth from year one was hard to miss. The inaugural 2025 event drew 680 attendees and 39 sponsors. This year? Over 1,000 attendees and 51 vendors showed up — and the majority of those attendees were end users. Not consultants. Not salespeople. Manufacturers.

The energy was bigger and better across the board. The move to the Hyatt Regency (connected to Union Station and Reunion Tower) drew overwhelmingly positive feedback from attendees. And while the conference team collected plenty of constructive input, what stood out was how consistent that feedback was — the same themes kept coming up, which means the team knows exactly what to sharpen for 2027. That's the mark of a maturing event: not zero complaints, but clear signal on where to level up next.

Here's what makes ProveIt! different from every other event on the industrial calendar: every participating vendor connects to a shared, live digital infrastructure. That infrastructure is built on a common Unified Namespace (UNS). Vendors don't get to hide behind marketing decks or canned demos running on a laptop. They publish, subscribe, and interact with real data in simulated factory environments — in real time, in front of everyone.

If your solution works, great. If it doesn't? Well, that's why it's called ProveIt!

The moment everyone will remember: Arlen Nipper receives the inaugural Proved It! Lifetime Award

Before we get into announcements and sessions, let's talk about the moment that stopped the room.

Arlen Nipper — the co-inventor of MQTT — was named the inaugural winner of the Proved It! Lifetime Award for his contributions to the invention of MQTT and his lifelong dedication to openness in software design. It was an emotional moment for everyone in the room, and absolutely well deserved.

If you work in manufacturing and your data moves, there's a very good chance MQTT is moving it. The protocol Arlen co-created has become the connective tissue of modern industrial architectures — from edge devices to UNS brokers to cloud platforms. His insistence on open, lightweight, publish-subscribe communication helped lay the foundation for everything ProveIt! is built on.

There's a fitting poetry to it: the man who helped invent the protocol that makes industrial interoperability possible, being honored at the conference that demands vendors prove that interoperability works. If that doesn't capture what ProveIt! is about, nothing does.

The big announcement: CESMII drops the i3X API

One of the most significant developments to come out of the week wasn't from a single vendor booth — it was from CESMII (the Smart Manufacturing Institute). They used ProveIt! 2026 to unveil the i3X API — the Industrial Information Interoperability Exchange.

Here's the problem i3X is solving: manufacturers are drowning in platforms. You've got historians, MES, quality systems, maintenance platforms — all with their own proprietary APIs. If an app developer wants to build something useful (say, an analytics dashboard or an AI-driven quality tool), they have to pick which vendor's API to build against. That means the app only works on that platform. No portability. No ecosystem.

i3X changes the game. It's a vendor-agnostic, open API that any contextualized manufacturing information platform can implement. Think of it as what iOS and Android did for mobile apps — but for the factory floor. One common interface contract, so application developers can build once and deploy across any compliant platform.

What makes i3X especially notable is how it came together. Multiple software vendors from across the industry collaborated on its development — competitors who agreed that the industry needs a common standard more than any one of them needs a proprietary moat. The contributors bring over 50 years of combined experience designing, developing, and implementing manufacturing information software across platforms like Rockwell, OSI Pi, ThinkIQ, ThingWorx, and HighByte, and across ecosystems including OPC UA, MQTT, Sparkplug/B, and Asset Administration Shell. The RFC went through a private review with more than 60 CESMII members before going public.

That kind of cross-vendor collaboration doesn't happen often. When it does, pay attention.

The API is currently in pre-release alpha (expect the 1.0 release to stabilize in Q1 2026), with a public demo endpoint, a Swagger page, a Python client library, and an i3X Explorer GUI from ACE Technologies. The whole thing is being developed as a public RFC — meaning anyone can review, contribute, and help shape the standard on GitHub.

CESMII also brought their Smart Manufacturing Profiles (SM Profiles) to the floor alongside i3X, with a keynote from John Dyck and Jonathan Wise exploring how standards-based interoperability and UNS-based approaches are finding more common ground than many in the industry expected.

Why this matters: If i3X gains adoption, it could unlock a manufacturing app ecosystem that doesn't exist today. Instead of being locked into one vendor's stack, you'd have portable analytics, visualization, and ML tools that work across platforms. That's a massive shift — and it started at ProveIt!

Sessions that stood out:

The week featured main stage sessions, live vendor workshops, and Table Talks — smaller, off-stage discussions grounded in hands-on experience. While dozens of vendors brought strong demos, sessions from Inductive Automation, MaestroHub, Eukodyne, and Thred earned extra attention on the floor. Of course, many of the sessions were great — though a few vendors left us wanting. And honestly, that's the beauty of ProveIt! When you can't hide behind slides, the gap between "proven" and "promising" gets very visible, very fast.

A Thursday fireside chat on AI in Manufacturing featuring Walker Reynolds, Jeff Knepper, Zach Etier, Mark Freedman, Sam Elsner, and Magnus McCune cut through the hype and worked backward from desired outcomes to the foundations actually required to get there — a refreshing change from the "just add AI" narrative you hear everywhere else.

Couldn't make it? All of the sessions will be published and available to watch over the next few weeks. Keep an eye on proveitconference.com for links.

And yes — there was a Medieval Times night. Because sometimes proving industrial interoperability and watching a jousting tournament in the same week is exactly the energy this industry needs.

Why it matters for manufacturing:

Here's the thing about ProveIt! that's easy to overlook if you weren't there: this event is quietly resetting the standard for how the industry evaluates technology.

For decades, manufacturers have been buying industrial software based on slide decks, reference calls, and vendor promises. ProveIt! flips that on its head. When every vendor plugs into the same shared namespace and has to demonstrate live data exchange, you get something rare — transparency. You can see which solutions play well together, which ones struggle with interoperability, and which ones are all talk.

The fact that the majority of attendees were end users tells you everything. Manufacturers are tired of being sold to. They want to see it work.

The growth from 680 to over 1,000 in just one year signals something bigger. The appetite for vendor-neutral, proof-based evaluation is massive — and it's only going to grow. If you're a technology vendor in the industrial space and you're not prepared to demo your solution on shared infrastructure, you should be asking yourself why.

The bottom line:

ProveIt! isn't just a conference — it's becoming the industry's proving ground. Between the i3X API launch, Arlen Nipper's well-deserved lifetime award, standout demos from vendors like Inductive Automation, MaestroHub, Eukodyne, and Thred, and a growing community that demands proof over promises, this event is setting the pace for how industrial technology gets evaluated. If you missed 2026, start planning for next year now.

Learn more about ProveIt! → | Learn about the i3X API → | Explore i3X on GitHub →

Amazon's AI Coding Tool Decided to "Delete and Recreate" a Production Environment. It Went About as Well as You'd Expect...

If you've ever watched Silicon Valley, you might remember when Gilfoyle's AI assistant was tasked with fixing a bug — and decided the most efficient solution was to nuke the entire system. Or when it was told to order lunch for the office and responded by ordering an absurd mountain of hamburgers. We laughed because it was satire.

Turns out, HBO was just a few years early.

The details:

According to a Financial Times report, Amazon Web Services experienced at least two outages in December tied to its own AI coding tools. The bigger incident? Engineers allowed Kiro — Amazon's agentic AI coding assistant — to make changes to AWS Cost Explorer, a tool customers use to track their cloud spending.

Kiro assessed the situation and determined that the most efficient path forward was to delete and recreate the entire environment. The result was a 13-hour outage affecting customers in mainland China.

A second incident involved Amazon Q Developer, another AI tool, though Amazon says that one didn't impact customer-facing services.

Here's what makes this especially spicy: Kiro isn't your typical autocomplete copilot. It's an agentic AI — meaning it can plan multi-step tasks and execute them autonomously. Give it a goal, and it figures out how to get there on its own. In this case, "get there" meant burning the house down and rebuilding it from scratch.

Amazon's response? "This brief event was the result of user error — specifically misconfigured access controls — not AI."

Translation: It's not the robot's fault. The human gave the robot too many keys.

And technically, they're not wrong. The engineer involved had broader permissions than intended, and Kiro was treated as an extension of the operator — meaning it inherited whatever access the engineer had. There was no mandatory peer review. No second set of eyes. No guardrails preventing an AI agent from making destructive changes to production infrastructure.

Those safeguards? AWS only implemented them after the outages. Mandatory peer review for production access and additional staff training were added retroactively. That timing makes the "user error, not AI error" defense feel a little thin.

Why it matters for manufacturing:

Here's where this gets real for your plant floor. Manufacturing is adopting AI agents too — for predictive maintenance, quality inspection, process optimization, even autonomous control loops. And the same fundamental question that tripped up AWS applies to every factory deploying agentic AI:

What happens when you give an autonomous agent the same permissions as your best engineer — but none of the judgment?

Real-world scenario: Imagine an AI agent monitoring a batch process notices an anomaly in your historian data. It has write access to the control system. It determines the "most efficient" fix is to reset the batch. At 2 AM. On a Sunday. With no human in the loop.

That's not science fiction. That's the logical outcome of deploying agentic tools without proper guardrails.

The lessons from AWS are directly transferable:

• Permission scoping matters. AI tools should never inherit full operator access by default. Principle of least privilege isn't just an IT security concept — it's an OT survival strategy.
• Peer review isn't optional. Any change to a production environment — whether initiated by a human or an AI — should require a second approval for destructive actions.
• "Agentic" doesn't mean "unsupervised." The whole point of agentic AI is that it can act independently. That's powerful. It's also dangerous without boundaries. Think of it like giving a new hire full admin access on day one — you wouldn't do it for a person, so don't do it for a bot.
• Blast radius control is everything. Even Amazon admits the incident was contained to a single service in one region. In manufacturing, you need to ask: if my AI agent makes a bad call, how far can the damage spread?

One senior AWS employee summed it up: "The engineers let the AI agent resolve an issue without intervention. The outages were small but entirely foreseeable."

Entirely foreseeable. Let that sink in.

The bottom line:

AI agents are coming to the factory floor — and they should. The productivity potential is real. But the AWS incident is a flashing warning sign: agentic AI without guardrails isn't innovation. It's negligence. If Amazon — with all its engineering resources — can get burned by an unsupervised AI agent, your plant can too. Build the guardrails before you hand over the keys.

Read the full report from The Decoder → | Financial Times original reporting → | Engadget coverage →

Cloudflare's Automated Cleanup Bot Deleted 1,100 Customer Network Routes. The Bug? A Missing Value in a URL.

Three days ago — literally last Thursday — Cloudflare experienced a 6-hour outage that took down chunks of the internet for customers who route their own IP addresses through Cloudflare's network. Websites went dark. Applications became unreachable. Connection attempts just... timed out into the void.

The cause? An automated cleanup script with a bug so subtle it passed code review, passed testing, and sat in the codebase for 15 days before detonating in production.

The details:

Cloudflare offers a service called BYOIP (Bring Your Own IP) that lets customers route their own IP address blocks through Cloudflare's global network. Think of it like telling the internet, "Hey, if you're looking for traffic headed to these addresses, send it through Cloudflare first." It's used for CDN, DDoS protection, and security services by some of Cloudflare's most sophisticated customers.

As part of an ongoing reliability initiative called "Code Orange: Fail Small" (more on the irony in a second), Cloudflare engineers built an automated sub-task to handle a previously manual process: cleaning up BYOIP prefixes that customers had flagged for removal.

Here's where it goes sideways. The cleanup script queried Cloudflare's internal API like this:

/v1/prefixes?pending_delete

See the problem? No? Neither did the code reviewers.

The parameter pending_delete has no value assigned. The API was designed to check: "Is the value of pending_delete not empty? If so, return only prefixes flagged for deletion." But since the value was an empty string, the API skipped that filter entirely and returned every single BYOIP prefix in the system.

The cleanup bot then did exactly what cleanup bots do. It started deleting. All of them. Systematically.

Before engineers identified and killed the runaway process, 1,100 out of 4,306 BYOIP prefixes — roughly 25% — had been withdrawn from the internet via BGP. Customer websites, Magic Transit protections, Spectrum proxy services, and even part of Cloudflare's own 1.1.1.1 DNS resolver were knocked offline.

Recovery wasn't simple either. The deleted prefixes weren't all broken the same way:

• Some just had their routes withdrawn — customers could toggle them back on via the dashboard
• Some had routes withdrawn and partial service bindings removed — partial recovery only
• Some had everything wiped — routes, bindings, configurations — requiring engineers to manually rebuild and push a global configuration update to every machine on Cloudflare's edge

That last group took until 23:03 UTC to restore. Over five hours after the initial impact.

The brutal irony? This change was part of Cloudflare's "Code Orange: Fail Small" initiative — a company-wide program launched after previous outages specifically designed to make configuration changes safer, more gradual, and easier to roll back. The automated cleanup was supposed to replace risky manual processes. Instead, it became the riskiest change they'd deployed in months.

Why it matters for manufacturing:

This is a masterclass in how automation can amplify small mistakes into catastrophic ones — and every concept here maps directly to industrial operations.

The empty parameter problem is everywhere in OT. Think about how many automated routines in your plant depend on query logic, filter conditions, or flag-based triggers. A recipe management system that queries "show me all batches flagged for disposal" and accidentally returns all active batches. A tag cleanup script in your historian that's supposed to archive decommissioned sensors but instead targets your entire tag database. The pattern is identical.

The "fail small" paradox is real. Cloudflare was actively trying to improve reliability when this happened. The automation was the safety improvement. Manufacturing teams face this exact tension: you automate a manual process to reduce human error, but now you've created a new failure mode that can execute at machine speed with no human in the loop. The error surface doesn't shrink — it shifts.

Here's what to take away:

• Default-deny beats default-allow. When a filter returns nothing, your system should do nothing — not everything. This is a design principle that applies to PLC logic, database queries, SCADA commands, and API calls equally. If a query returns an unexpectedly large result set, stop and ask why.
• Test with production-scale data. Cloudflare's staging environment didn't catch this because the test data didn't match real-world conditions. If your staging historian has 50 tags and production has 50,000, you're not testing — you're guessing.
• Automate the rollback, not just the deployment. Cloudflare admitted they didn't have a fast way to snapshot and restore operational state. The recovery took hours because engineers had to manually reconstruct configurations. If you can deploy a change in seconds, you need to be able to undo it in seconds too.
• Time-delay destructive actions. A 15-minute hold on any bulk delete operation would have given engineers time to notice 1,100 prefixes disappearing. The same applies to your batch systems, recipe management, and tag databases. Never let automation execute mass deletions in real-time.

The bottom line:

Cloudflare's outage wasn't caused by a cyberattack, a hardware failure, or even bad architecture. It was caused by a missing value in a URL parameter — one character's worth of context that turned a targeted cleanup into a scorched-earth campaign. In manufacturing, we'd call that a recipe parameter error. And on a production line, recipe parameter errors don't just cost uptime. They cost product, money, and sometimes safety. The lesson: every automated process that can delete, modify, or stop something needs a circuit breaker. Period.

Read Cloudflare's full post-mortem → | Cloudflare's Code Orange: Fail Small initiative →

A Word from This Week's Sponsor

Litmus — The Infrastructure Behind Industrial AI

Last week at ProveIt!, Litmus delivered one of the most compelling demonstrations of the entire event.

As a Title Sponsor, they didn’t just talk about AI.
 They demonstrated how modern industrial infrastructure becomes the foundation upon which AI-native applications can actually run.

Industrial AI doesn’t fail because of models.
It fails because of infrastructure.
Disconnected PLCs.
Fragmented OT data.
Cloud-first architectures that ignore edge reality.
That’s the gap Litmus is built to solve.

Litmus Edge is a complete edge data platform designed to simplify OT-IT data pipelines and make industrial AI possible at scale.
With 250+ industrial connectors and no-code integration, Litmus enables manufacturers to:
• Connect and process real-time OT data from virtually any system
• Contextualize and normalize data at the edge — not in post-processing
• Deploy analytics and AI with low latency and high reliability
• Scale across sites without losing governance or control

This isn’t about sending more data to the cloud.
It’s about creating structured, contextualized intelligence at the edge — where operations actually happen.

What stood out at ProveIt! was how Litmus embeds AI inside context-aware industrial architecture.
From real-time data collection to centralized management to AI deployment, the platform is built for production environments — not lab demos.

And for engineers who want to get hands-on, the Litmus Edge Developer Edition provides full platform access with a resettable license. No watered-down trial. No artificial limits.

If your organization is serious about bridging OT and IT — and building infrastructure that AI can actually depend on — Litmus is a platform worth understanding.

 Want to kick the tires on Developer Edition?

Link Here: https://litmus.io/litmus-edge-developer-edition

Stop Thinking of AI as a Coworker. It's an Exoskeleton.

We just spent two articles watching what happens when AI tools are turned loose without guardrails. Amazon's Kiro deleted a production environment. Cloudflare's cleanup bot nuked 25% of customer network routes. In both cases, the AI was treated like an autonomous coworker — given a task, given permissions, and left to figure it out.

Here's the thing: that's the wrong mental model. And manufacturing already has a better one.

The details:

A recent piece from Kasava makes a compelling argument that's been gaining traction across the tech industry: AI shouldn't be thought of as a coworker that works independently. It should be thought of as an exoskeleton that amplifies what you can already do.

This isn't just a clever metaphor. It's a design philosophy — and manufacturing is arguably the industry best positioned to understand it, because you've been living it.

Physical exoskeletons are already on the factory floor. Ford deployed EksoVest upper-body exoskeletons across 15 plants in 7 countries. Their assembly workers lift their arms overhead up to 4,600 times per day — roughly a million times per year. The EksoVest provides 5-15 lbs of lift assistance per arm, transferring that load from the shoulders down to the hips. The result? An 83% decline in worker injuries. Boeing saw a 17% boost in production speed after deploying the same technology on their 787 Dreamliner line.

Here's what makes the exoskeleton model so powerful — and why it matters for how you deploy AI:

The exoskeleton doesn't replace the human. It doesn't lift the boxes on its own. It doesn't decide which boxes to lift. It doesn't walk itself to the warehouse. The human is still doing the work — they're just doing dramatically more of it, more sustainably, with less strain. The human stays in control. The machine handles the burden.

Now apply that to AI.

Where the "AI as coworker" model breaks down:

The tech industry has been chasing "agentic AI" — systems that operate autonomously, make their own decisions, and complete entire workflows without human intervention. The dream is seductive: an AI employee that just handles things.

But as Kasava's essay points out, autonomous agents fail precisely because they don't carry the context that humans carry around implicitly. They don't know that your enterprise clients care more about reliability than speed. They don't know that Line 3 was running hot last week and the bearings might be marginal. They don't know that the reason you run that batch at 72°F instead of 75°F is because of a quality issue three years ago that never got written down anywhere.

That implicit context — the stuff that lives in your operators' heads, in tribal knowledge, in handwritten notes on whiteboards — is exactly the kind of judgment AI agents lack. And when they act without it, you get the AWS and Cloudflare incidents we just covered.

A Harvard Business School study with Boston Consulting Group consultants found that AI users completed 12% more tasks, 25% faster, and at 40% higher quality — but only when the tasks fell within the AI's capability frontier. When tasks required judgment beyond what the model could handle, consultants who relied heavily on AI actually performed worse than those who didn't use it at all.

Read that again. AI amplifies competent humans. It degrades the work of those who defer to it uncritically.

That's the exoskeleton principle in action.

What this looks like on the factory floor:

Think about where AI is being deployed in manufacturing right now: predictive maintenance, quality inspection, process optimization, demand forecasting, energy management. In every one of these cases, the highest-performing implementations share a common pattern:

• Predictive maintenance: AI flags that a bearing signature looks anomalous and surfaces it to the maintenance team. The human decides whether to pull the machine, schedule it for the next planned downtime, or monitor it for another shift. The AI handles the data volume no human could process. The human provides the operational judgment.
• Quality inspection: AI-powered vision systems catch defects at speeds no human inspector can match. But the human sets the acceptance criteria, interprets edge cases, and decides when to adjust the process rather than just reject parts.
• Process optimization: AI analyzes thousands of parameter combinations to suggest optimal setpoints. The engineer evaluates whether those suggestions account for upstream variability, material lot differences, and equipment wear that the model might not see.

In each case, the AI is the exoskeleton. The human is still doing the work. They're just able to process more information, catch more issues, and make better decisions — faster and more sustainably.

How to apply this thinking:

If you're evaluating AI tools for your operation, ask one question before anything else: "Is this tool designed to amplify my team, or replace their judgment?"

• Amplify: AI surfaces insights, flags anomalies, suggests options, pre-processes data, drafts reports. Human reviews, decides, acts. This is the exoskeleton.
• Replace: AI detects issue, decides on action, executes change, reports after the fact. Human is informed, not consulted. This is the autonomous agent.

There's a place for automation. Nobody's arguing that your fill-to-level sensor needs a human in the loop. But for complex decisions in variable environments — which is most of manufacturing — the exoskeleton model wins.

The bottom line:

Manufacturing already understands this better than Silicon Valley does. You've been strapping physical exoskeletons onto workers for years — not to replace them, but to make them stronger. Apply the same philosophy to AI: amplify the human, don't replace the judgment. The best AI deployment on your factory floor will be the one where your operators say, "I can't imagine going back to doing this without it" — not the one where they say, "I don't know what it's doing, but it seems to be working."

Read the original Kasava essay → | The Exoskeleton Theory: Amplifiers, Not Replacements (WebProNews) → | Anthropic's Research on Measuring AI Agent Autonomy →

Byte-Sized Brilliance

In 2010, then-Google CEO Eric Schmidt dropped a statistic that still lands like a punch: from the dawn of civilization through 2003, humanity created roughly 5 exabytes of data. Cave paintings, the Library of Alexandria, every book ever printed, every film ever shot, every record ever pressed — all of it. Five exabytes.

We now create that much data every two days.

If you were at ProveIt in Dallas last week, you probably heard Jeff Winter reference this during his keynote — and if you felt the room get a little quiet for a second, that's why. It hits different when you're sitting in a room full of people who work with industrial data every day.

The underlying research came from a UC Berkeley study called "How Much Information?" — and while some have quibbled with Schmidt's exact math, the directional truth is undeniable. As of 2024, the world generates roughly 402 million terabytes of data per day. That's about 147 zettabytes per year — and we're on pace for 230+ zettabytes in 2026.

Here's where it hits home for this audience: McKinsey Global Institute identified manufacturing as the single most data-prolific industry on the planet, generating an average of 1.9 petabytes per year. A typical factory produces about 1 terabyte of production data every day — temperature readings, vibration curves, pressure logs, cycle times, defect counts. Your machines are basically journaling their entire lives.

And yet, according to IBM, 90% of that factory data goes completely unused. Never analyzed. Never queried. Never even opened. It's like running a library where you burn 9 out of every 10 books before anyone reads them.

That 90% gap is exactly why the exoskeleton model from Article 4 matters so much. No human can sift through a terabyte of vibration data looking for a bearing signature drifting 0.3% per week. But an AI tool can — and surface that insight to the one person on your team who knows what to do about it. We never had a data problem. We had a human bandwidth problem. Now we're finally building the exoskeleton for that, too.

Speaking of Building the Right Toolkit...

This entire newsletter has been about one idea: the tools you choose matter as much as the problems you're solving. AI that operates as an exoskeleton instead of an unsupervised agent. Automation scripts that default to deny instead of delete-everything. Proof over promises.

That's exactly why we built 40solutions.com — the app store for industrial solutions.

We got tired of watching engineers waste weeks evaluating software they couldn't even test without sitting through three sales calls and a demo that shows everything except the thing you actually need. So we built a one-stop shop where you can discover, compare, and deploy Industry 4.0 solutions from vetted vendors — connectivity, analytics, automation, visualization — all with transparent pricing and free trials.

No hidden fees. No "contact us for a quote." No surprise phone calls from a BDR named Chad.

We just launched, and we're being honest about that — the first product is live now with about a dozen more on the way. Every vendor is reviewed by our team of Industry 4.0 practitioners before they're listed. You can download a free trial, get hands-on with the actual product, and then decide if it earns a spot in your production environment. Try it, prove it, buy it — in that order.

We're building this thing in public, and we'd rather launch lean and grow with quality than stuff the shelves with junk. Browse what's available today at 40solutions.com — and if you're a vendor building tools that help manufacturing teams solve real problems, we want to hear from you too.