You already have a way of doing this.
Every brewery we talk to is already doing this somehow. A spreadsheet. A data historian. A generic analytics platform. A plan to build something in-house. All four can work, to a point. Here's where each one runs into trouble, and where PLAATO Insights doesn't.
Spreadsheets and paper
This is where almost every brewery starts, and it's a reasonable place to start. A PLC already tracks what's happening in the moment. The rest, lab results, manual checks, what happened on last night's shift, goes into a spreadsheet or a notebook.
It works, right up until it doesn't. PLC data is ephemeral: unless someone is actively pulling it off and storing it, it's gone. Spreadsheets don't talk to each other, so quality data, process data, and maintenance notes stay in three different places, remembered by three different people. And everything in a spreadsheet is retrospective. You find out about the spillage after the batch is gone, and the drifted batch after it's already off spec.
None of this is a problem with a handful of people who all know everything. It becomes one the moment a brewery grows past that.
Your existing control system, SCADA or historian
We're not asking you to rip this out. PLAATO connects through the PLAATO Edge Gateway using the protocols already running on your floor, OPC UA, Siemens S7, Modbus TCP/IP, and it sits alongside your control systems rather than replacing them.
The gap isn't the PLC or the historian itself, it's what happens to that data afterward. A historian is built to collect raw, single-point data. It wasn't built to tell you that a pump's current draw has drifted 12% from its own baseline over three weeks, or to line that reading up against the batch, the lab result, and the work order that all touched it that day.
Same PLCs, same protocols, same floor. The data just stops living in one screen you have to already know to check.
A generic manufacturing analytics platform
Plenty of platforms out there do real, credible analytics work across factories, warehouses, and plants of every kind. That breadth is exactly what makes them slow to get useful on a brewery floor.
A tool built to serve every kind of manufacturer doesn't know a mash mixer from a centrifuge out of the box. It doesn't structure data the way a batch brewing process actually runs, and it can't read a brew sheet. Getting there takes months of configuration, mapping generic tags and templates onto what a brewery actually does, batch by batch.
PLAATO's data model is built on ISA-88, the batch-process standard, from day one, because brewing is a batch process. Our AI scanners are trained specifically on brewing documentation, so they read a brew sheet correctly the first time instead of the twentieth.
Building it yourself
Some breweries have the engineering talent to build a piece of this. Very few have the time to build all of it, and keep building it as equipment, staff, and sites change.
Pulling data off a PLC is the easy part. Storing it indefinitely, structuring it around your actual batches and assets, teaching a system what your equipment's normal looks like well enough to catch a drift before it becomes a failure, and then maintaining all of that as your brewery changes, that's a standing engineering commitment, not a project with an end date.
Build it yourself, or bring in PLAATO
| Build it yourself | PLAATO Insights | |
|---|---|---|
| Scope | Solves for one process, one team, one system, as narrowly as you scope it | One platform, already proven across several breweries in globally |
| Scaling | Re-engineered for every new site, asset, team or use case | Same platform extends across assets, teams and sites as you grow |
| Logic | Rules and thresholds you write and validate yourself, from nothing | Brewing-specific logic built in: ISA-88 structure, baseline-drift detection, recipe-level alerts |
| Maintenance | Your team owns every fix, upgrade, and edge case, indefinitely | PLAATO's team maintains and improves the platform underneath you |
| Timeline | Months to years before the first genuinely useful output | Weeks to first dashboards, alerts, and a defined scope, through a 8-week pilot |
| Cost | At minimum, a data or integration engineer plus a data scientist, full time, indefinitely. At least two additional FTEs in salaries alone, before recruiting, onboarding, and the time it takes them to catch up | Always less than the cost of hiring the team on the left, with none of the hiring, onboarding, or retention risk |
Three questions to ask any vendor, including us
Does it work with what's already on your floor, or does it ask you to rip it out?
If the answer involves replacing your PLCs or your historian, that's a much bigger project than the one you're actually trying to solve.
Is it actually built for brewing, or is brewing a label on top of a generic industrial tool?
Ask how it structures batch data, and whether it's ever read a real brew sheet.
What's proven, and what's still being proven?
Ask for the specific number, and how it was measured. A vendor who can tell the difference is one worth trusting with the rest.
Where we stand on proven vs. proving
We'd rather tell you what's actually proven than imply everything is.
Increased throughput. Improved quality and consistency. Daily time savings. Raw material savings. Reduced spillage and waste.
Yield increase. Predictive maintenance. Water loss. Wastewater volume reduction. Energy cost reduction.
Still deciding what to compare us against?
Let's walk through your setup.