Defects are missed on the line and found by the customer
See
Example
Every part inspected by a camera trained on your own rejects, flagged with a confidence and corrected in one tap.
Industries · Manufacturing
Defects, anomalies and downtime, caught before they cost you.
Vision on the line, anomaly detection on the sensors and forecasts on the machines, running in your plant and corrected by your operators.
Typical problems
Three problems we see every time.
Downtime is explained after the fact
Predict
Example
A failure forecast from vibration, temperature and cycle data, with the maintenance ticket raised early.
Anomalies are buried in sensor data nobody reads
Detect
Example
Readings outside their normal range flagged to the shift lead, with the last hour of context attached.
The case
One system, in production.
Manufacturing
Sand's engineers stopped guessing why a line stopped.
A system that watches the production line and explains a stoppage before an engineer has to ask.
-70%less time to find why a line stopped
100%stoppages logged automatically
What is specific here
The data and the rules of the sector.
A factory does not stop for a model. What is specific here is running on the line, on your hardware, at line speed, and leaving a trace for every part so quality, maintenance and audits can rely on it.
- Traceability
Every decision the system makes is tied to the part, the batch and the moment, so a recall or an audit can trace it.
- Edge deployment
Inspection runs on the line, on your hardware, without depending on a connection that a factory floor cannot guarantee.
- Line integration
The system talks to the PLC, the MES and the shift's screens it already has, and never slows the line to do it.
Who we work with
Teams that ship with us.
- Sand
Before you ask
Questions from this sector.
Does it run on the line without an internet connection?
Yes. Inference runs on hardware on the line and keeps working when the connection drops; results sync when it returns.
How much labelled data do we need?
Less than you think, and mostly what you already have. We start from your rejects and your logs, and operators label the rest in one tap as the system runs.
What happens when the model is unsure?
The part goes to a person. A low-confidence result is never silently accepted, and each correction becomes a labelled example for the next retraining.
Let's build what comes next. Together.
Tell us what you are building and what changes if it works. You get a straight answer on whether AI can solve it.
Talk to usor write to hello@maistik.studio