Unplanned downtime
A machine fails without warning, and the whole line waits. Every stoppage costs more than the repair, it costs the output you didn't make.
Industries
Downtime is expensive, most of it doesn't have to happen.
A machine fails without warning and the whole line waits. Defects slip through until a customer finds them. Inventory gets ordered on a gut feeling. Each of those is a cost your plant is quietly absorbing every week, and each one is exactly where AI earns its keep, once it's proven on your own data first.
30-50%less unplanned downtime with predictive maintenanceIllustrative range
1,000+/minunits inspected for defects at full line speedIllustrative range
Fewerbatches scrapped, caught before a run finishes instead of afterIllustrative range
4-8 wksadvance warning before a bearing or motor actually failsIllustrative range
Where it hurts
The costs that get absorbed instead of fixed.
None of these show up as a single line item, they show up as slower quarters, tighter margins, and teams that are always reacting instead of ahead of the problem.
Manual quality inspection
Eyes get tired, shifts change, standards drift. Manual inspection alone lets defects through and catches others too late to matter.
Supply & inventory guesswork
Ordering by gut feel means either cash tied up in excess stock, or a line stopped waiting on a part that should have been on hand.
Reactive maintenance & paperwork
Maintenance happens after something breaks, not before, and someone still keys in POs and chases spec sign-offs by hand, hours a week on work that isn't the work.
Where AI pays off
Six places manufacturers see a return fast.
We don't start with the technology. We start with your line, your data, and your bottlenecks, then point AI at the spots where it earns its keep.
01
Predictive maintenance
Sensor drift flags a failing motor or bearing four to eight weeks out, so service happens on a schedule you choose, not one a breakdown chooses for you.
02
Computer-vision inspection
Cameras and a trained model catch sub-millimetre defects at full line speed, consistently, shift after shift, no eyes getting tired.
03
Demand & supply-disruption forecasting
Forecasts built from your own history plus external signals, so you see a supplier delay or a demand swing before it hits the schedule.
04
Digital twins
Test a line change, a new part, or a schedule shift in a model of your process before you touch the actual floor.
05
Self-optimizing parameters
Live sensor and quality data tune process settings continuously, holding the line closer to spec without a manual adjustment.
06
Document extraction
Purchase orders, specs and compliance paperwork get read and routed automatically, no more re-keying the same data twice.
See it before it happens
Problems get flagged while they're still cheap to fix.
Vibration, temperature and quality signals get watched continuously, so a bearing trending toward failure or a batch drifting out of spec shows up as an alert on someone's screen, not a shutdown on the floor.
Estimate what avoided downtime is worth
Your systems, connected
One source of truth, not six.
AI sits across your ERP, MES, sensors and supplier data, so a change in one place updates everywhere else, instead of someone re-entering the same numbers by hand in a second system.
Built to be safe to say yes to
We take the risk out of adopting AI on your line.
The nervousness is fair, plenty of AI projects overpromise and underdeliver. Here's how we make sure yours isn't one of them.
Private & auditable
Your production data, specs and machine logs stay under clear guardrails, with an audit trail for every decision the system makes.
You own everything we build
The models, the code, the integrations, they're yours. No lock-in, no hostage situations if you ever want to move on.
Vendor-neutral
We're not tied to one platform or one sensor vendor. We fit the tools to your line, not the other way around.
Proven small before scaling
Every build starts with a proof-of-concept on one line or one process, validated on your real data, before it rolls out further.
Questions manufacturers ask
What most plants want to know before they start.
Do we need new machines or sensors to get started?
Usually not. We start with what you already have, existing PLC data, historian tags, and any sensors already on the line. If a real gap needs filling before a use case works, we'll say so plainly instead of assuming a large hardware spend.
Will this integrate with our existing ERP or MES?
Yes, that's the point. We build AI to sit alongside your ERP and MES and read from them, not to replace them. No rip-and-replace, no re-entering the same data in a second system.
How fast do we see a first result?
Every engagement starts with a proof-of-concept on one line or one process, using your real data, typically weeks rather than months. You see whether it works before we talk about rolling it out further.
Keep exploring
Related reading
The AI use-case library
Concrete ways AI takes work off your plate, organised by what you are trying to fix.
Browse use casesQuestions to ask first
The questions that reveal where AI will actually pay off, before you spend a thing.
Read the guideSee the numbers
Estimate the hours and money AI could give your team back each year. Free, no signup.
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Start the conversation
Let's find where AI moves the needle on your line.
Book a $150 strategy consultation, or run the numbers yourself first. We'll talk through your operation, spot the fastest opportunities, and tell you honestly what's worth doing.