AI is only as good as its data and its context, so we built both first.
MACH is AI native: Virgil, its read-only assistant, answers from your plant’s own data and context and cites every number. Point a model at raw machine logs and it'll answer confidently, and often wrong, because the logs never say why anything stopped. MACH captures the reason the operator gave, and what each number means in your plant. Virgil, the assistant on every dashboard, works from both.
Data
Stops carry the operator's reason, rejects are tied to their order, and changeovers are timed as they run, all in one history.
Context
Every plant has its own definition of OEE, and its own KPIs for the processes it actually runs. You settle what each one means here, and Virgil reads that before it answers.
Ask Virgil
Ask a question on any dashboard and get an answer from your own production data. Open the query behind any number it gives you.
Governance
Virgil reads. It never edits your production history. When it suggests a chart or a project, a person decides whether to keep it.
Last updated: October 14, 2026
Machine data a model can actually answer from.
A machine log will tell you the press stopped at 9:12. It won't tell you why, which order was sitting behind it, or who was running it that shift. Those are the parts a model needs, and MACH captures them at the machine, while the job is still running.
Pieces, tons, linear feet, whatever the process runs in.
Output is counted in the machine's own units rather than forced into a generic part count, so the answer fits the process that produced it.
Classified at the machine while it's still stopped.
The operator taps a category and a reason before the machine is back up, tied to the shift and the order it held up.
Logged against the order they came off.
Scrap carries the order, the material, and the machine, so yield by product is something you look up rather than reconcile at month end.
Timed while they run.
The clock starts when the last order finishes and stops when the next one is up to speed, with nobody holding a stopwatch.
Every operation defines its own numbers. Virgil answers in yours.
Every plant has its own OEE formula and its own idea of what "on pace" means, and a general-purpose model knows none of it, so it guesses. In MACH you choose every formula and definition. We bring recommendations from years on the floor, but you make the call, and the reasoning behind it stays with the KPI. Virgil reads that reasoning whenever it works with the KPI.
It knows what Press 2 is.
Virgil knows the plants, work centers, machines, stations, and shifts, so it can tell which line a machine sits on without being told.
Your formula, in your words.
Each KPI carries the reasoning behind it, not just the math, so Virgil explains a number the way your team would.
It starts where you're looking.
The plant, machines, and date range on your dashboard are already the scope when you start typing.
How the plant actually runs.
A few paragraphs about the plant, like which lines matter most and what a normal week looks like. Virgil reads them before every answer.
Ask the question you'd ask your best analyst, and have the answer before the meeting.
Virgil is MACH's AI assistant, on every dashboard and in every CI project. It already knows the plant, machines, and date range you're looking at. Ask which machine improved the most last month, or why night shift keeps trailing day shift on Press 2.
Here's what you're seeing: Virgil answers an open question like “how did yesterday go?” and then works through a what-if, showing how much a change would save.
- Ask a follow-up in plain words, like “now just night shift,” and Virgil narrows the same answer.
- Answers come back as tables and charts as well as text, ready to drop into the meeting deck.
- Ask the same question every week? Turn it into a scheduled report, and the answer arrives on its own.
- When a number moves, ask why, and Virgil digs into the stops, shifts, and orders behind it.
What people ask it.
Rank it, slice it, compare it.
Which machine improved the most, the three longest stops last week, night shift against day shift on Press 2, or Q1 against Q2 against month to date in one answer.
Where's GEN-7?
Order status, what's next on a machine, which orders due this week won't make it, and where there are four free hours on Thursday. Same math as the schedule board, in plant time.
If we halved feed jams?
Virgil adjusts the inputs to your own OEE formula and says what last month would have looked like, clearly labelled as a projection rather than a measurement.
Send me the file.
Every stop over ten minutes this quarter, as a spreadsheet you can open in Excel and take into the next meeting.
Questions that cross plants or machine types go to Explore, where Virgil can reach every MACH app and all your data, not just one dashboard's. When an answer would make a good chart, Virgil proposes it for the dashboard. When it lands on a loss that keeps coming back, it offers to start a CI project. Either way, you're the one who clicks to keep it.
Some questions you ask once. Others you want answered every Monday at 5am.
Virgil can run on a schedule, so the question you'd normally type at 7am on Monday is already answered and waiting. There are two kinds, because "send me the report" and "tell me if something's wrong" are different asks. If you'd rather lay the report out yourself, the report builder takes your dashboard charts and lets Virgil write only the summary.
A report delivers every time.
The prior-week ranking lands in your inbox before the production meeting, findings on top with the charts and tables underneath.
A monitor speaks up only when something is wrong.
It stays quiet while things are on track. When they aren't, it tells you once and then holds off, instead of repeating itself every hour.
- Results land in MACH for the people they were sent to, with a conversation ready for the obvious next question.
- Floor and schedule alerts send on their own the moment a rule trips. Turn on Virgil for a rule and its read on the situation rides along in the same message.
Your data science team keeps its models and finally gets labeled inputs.
Read-only query APIs expose the same history to your BI tools, data warehouse, data lake, or in-house models. Your team can also ask Virgil from Claude, ChatGPT, or your own agent, with the same plant context and the same permissions.
Read-only query APIs.
Machine-day metrics, downtime with reasons, order steps, and schedule performance flow into the tools you already run. You build the models, and MACH supplies the labels they never had.
Claude, ChatGPT, or your own agent, over MCP.
Your admin approves the app and can revoke it at any time. Each person signs in as themselves, and the app sees only what they could see in Hub. Ask about KPIs, downtime, period comparisons, and schedule adherence from the tool your team already uses.
It reads. It never writes.
Virgil earns trust the way a new engineer does: it shows its work, and it can run its own scenarios without ever changing your actual data.
It can't change your data.
Virgil can't edit a schedule, change a KPI, or touch a record. It can propose a chart, a dashboard, or a CI project, and nothing is saved until a person clicks.
Your definitions apply on every query.
Every question goes through the same query engine your dashboards use, so your OEE formula and your plant's dates apply every time. Every figure in an answer comes from your plant's own data.
Every step is recorded.
Each query it ran and what came back stays with the answer. Open "How I got this" on any message.
MACH is building its own AI on this foundation.
The first pieces are live today: Virgil on every dashboard and inside Reports & Alerts and CI Projects, custom reports and monitors that run on a schedule, and MCP, so your own models can use MACH’s tools and plant context. Together they’re what make MACH AI native. Everything else on the roadmap reads from the same foundation, so none of it needs a new data project first.
AI native
Labeled floor data and your plant’s own context come first, so any model that reads from MACH, ours or yours, starts from answers it can check.
MCP for your own models
Claude, ChatGPT, or your own agent can use MACH’s tools and plant context over MCP, with the same access as the person asking.
Learned job durations
Durations come from what each job actually took on each machine, instead of the estimate in the routing.
Executive intelligence layer
Leadership gets insight cards rolled up across every site, built from each plant's own data.
Questions about MACH AI.
What does it mean that MACH is AI native?
MACH was built so labeled production data exists and so you can put it to work: every stop classified by the operator as it happens and linked to the machine, shift, and order it delayed, rejects logged against the order, changeovers timed as they run, and corrections kept with an audit trail. The data and the plant context both came before the model.
Does MACH have an AI assistant?
Yes. Virgil sits on every dashboard and in every CI project, answers from your own KPIs and production data, cites every number to a query you can open, and can run on a schedule as a report or a monitor. It's read-only.
Why not point a general-purpose AI model at our data?
Because the data alone isn't enough. Raw machine logs don't carry reasons, and a general model doesn't know your OEE formula or your reason codes, so it gives you a confident answer you can't check. MACH holds the plant context, answers every question with your own formulas and reason codes, and cites each number to a query you can open.
Is Virgil safe to run in a production plant?
Yes. Virgil is read-only. It can run its own scenarios, but it can't edit a schedule, change a KPI, or modify a record, and every step is recorded so you can open the trace on any answer.
Can Virgil answer questions about today's schedule?
Yes. Ask where an order is, what's next on a machine, which orders due this week are at risk, or where there's free time on Thursday. Virgil works from the same calculations as the schedule board, so the two can't disagree, and gives times in the plant's clock.
Can I connect Claude, ChatGPT, or my own model to MACH?
Yes, through MACH's MCP (Model Context Protocol) server. Your admin approves the app first and can revoke it at any time, each person signs in as themselves, and the app sees only what that person could see in Hub. It can ask about KPIs, downtime, period comparisons, and schedule adherence. If you'd rather pull the raw history, read-only query APIs expose it to your BI stack, warehouse, lake, or in-house models.
Does Virgil cost extra?
No. Virgil comes with every MACH product (Monitor, Schedule, or Complete) at no additional charge. See pricing.
The data is already being captured. See what it says about your floor.
We start with a Shop Floor Diagnostic. One-hour call to understand your situation, then a written assessment of where the losses are and what closing them would be worth. Where we can't help, we say so.