Running the world on time, efficiently and safely.
You monitor machines one at a time but the loss happens across the operation as a whole, and that whole is what nobody is watching. spiderAI™ is an agentic intelligence for industrial equipment that brings contextual intelligence 24x7 to ensure that the annual operational targets are achieved.
spiderAI™ is an equipment intelligence to run industrial operations on time, efficiently and safely. It can be accessed from conversational interfaces, web application and can also be deployed on the drones & robots for physical intelligence.
A month below plan, and nobody could say what to fix first.
This is a real sinter line at a steelworks. Over thirty days it delivered 9.7 days less output than planned. Fourteen of its fifty machines flagged a problem, three had gone silent for weeks and were counted as healthy, and on day twenty the line was stopped so a crew could open machines and look. Every fact needed to rank those fourteen already existed on site. Nothing put them in order. Step through the month to see how the gap built. Change the nouns and this is a fab, a desalination train or a data-centre hall.
100%90%50%NOTHING PRODUCED
0%
Nothing has failed. The line is already below the rate it is asked to hold, and nothing tells anyone where to look.
A concern has been raised, and the only way to learn what it means is to stop the machine and take it apart.
Seven machines share one electrical supply and show the same read. Nothing relates their readings to each other, so each is inspected as though it were alone.
These have sent no signature for 23, 39 and 66 days. Silence is not health, but nothing silent ever raises a concern.
The same fourteen, in no order. This is what is left after the line has been stopped, the machines opened and the morning gone.
EVERY MACHINE HERE IS FITTED TO SEND A CURRENT SIGNATURE, CONTINUOUSLY, WHETHER IT IS RUNNING OR NOT.
THIS IS A SINTER LINE AT A STEELWORKS. CHANGE THE NOUNS AND IT IS A FAB, A DESALINATION TRAIN, A DATA-CENTRE HALL.
The line is asked to hold one hundred per cent of its planned production rate. Across the thirty days shown it almost never held that rate: it runs just short of plan for the first week, falls through the second, sits near half rate in the middle of the month, and on day twenty production stops entirely so a crew can open machines and look for the fault. It climbs back unevenly and ends the month below plan. Fourteen of fifty machines have raised a concern, seven of those share one electrical supply, and three machines are sending nothing at all. At the end of the month the same fourteen concerns remain, in no order.
NOTHING HERE WAS UNKNOWN. IT WAS UNORDERED.
A month spent below plan, and nothing on the board that says what to fix first.
The 9.7 lost days are not a mystery. Fourteen machines raised a concern and stayed on the board unranked, three sent nothing for weeks and were carried as healthy, and the only way left to find the fault was to stop the line and open machines. Every fact needed to rank that morning already existed on site, held by different people, in different systems, on different clocks.
spiderAI™ is the equipment intelligence that puts every machine in the context of the whole operation.
On the sinter line above, that context is what ranks the fourteen concerns nobody could order, and calls the day’s production before any machine is named. It takes what the sensors measure, what is written in manuals, work orders and breakdown history, and what the people on the floor report back, and reasons across all of it, 24x7. That only became possible after large language models, and spiderAI™ is built to bring it to equipment. Its four parts mirror how a good plant team already works, which is why the same intelligence scales across industries.
Manuals, breakdown history, inspection notes, work orders, nameplates, drive-train data, the written procedure the operation runs by, and the answers people give it while they work. Everything goes in and gets structured; a person verifies every fact before it counts, and it answers only from what has been verified.
OUR IoT PRODUCT · iHz™ SYSTEMS
The world runs on machines, and machines run on power. iHz™ System is designed to capture intelligence from that power.
The current product series applies Motor Current Signature Analysis to equipment driven by three-phase AC induction motors, diagnosing faults to component level from one device installed at the electrical panel. No sensors on the machine, no production stop to install.
iHz™ feeds spiderAI™ as one signal source, alongside instrumentation the plant already runs.
The same month. Run at plan, no stop on day twenty, the fourteen ranked.
Here is what spiderAI™ decided on that line, day by day: the production call, the jobs for the shift, every machine placed with a next step, the fourteen concerns ranked by what each costs the line, the top job opened with its decision rule, the silent machines owned, and what is still open from last month. Ask again after the field reports back and the ranking updates.
What to do with production today, before any machine is named.
100%NOTHING PRODUCED
0%
- WITHOUT spiderAI™ · 9.7 DAYS LOST
- DAY 20 · LINE STOPPED, MACHINES OPENED, NO ORDER TO WORK IN
- WITH spiderAI™
- DAY 20 · LINE RUNS · THE ANSWER WAS WHICH MACHINE TO MEASURE FIRST, WHAT TO MEASURE, AND WHY THE OTHER THIRTEEN CAN WAIT
- DAY TWENTY · NO STOP · FOURTEEN CONCERNS, SEVEN OF THEM ON ONE SUPPLY, AND THREE MONTHS OF REPAIR HISTORY, WEIGHED TOGETHER, SAID NOTHING JUSTIFIED STOPPING THE LINE
- FOURTEEN CONCERNS · RANKED ONE TO FOURTEEN · BY WHAT EACH COSTS THE LINE · FOUR WORKED THIS SHIFT, TEN RUN ON WITH A JOB CARD
- THREE SILENT MACHINES · NAMED ON DAY ONE, EACH WITH AN OWNER AND AN ANSWER DUE BEFORE SHIFT END
One intelligence, many ways in.
The same reasoning reaches every person on site through the surface their job needs. A site head asks a question and gets an answer with its evidence. A planner gets a report for any area and any horizon. A technician gets a ranked finding opened into a job card. The workflow carries each finding to the person who may act on it. Seven surfaces, one intelligence underneath.
Ask anything about a machine. spiderAI™ asks who you are and which area you hold, because the same question from an inspector, an area head and the site head has three correct answers. Every answer is cited to verified evidence, or names the gap and the document that would close it. Nothing is guessed.
Every one of these is the same intelligence, seen from a different seat.
The machines are monitored.
The losses keep accumulating.
Across machines. Between processes. Beyond the reach of isolated measurements.
Lost even after IoT, SCADA and basic predictive maintenance are in place.*
Industry 4.0 initiatives miss business objectives.
McKinsey*Factories fail to implement predictive maintenance.
LLumin / 2025*Industrial operational data goes unused.
IBM*The cost is operational.
The missing piece is context.
*Figures carried over from the current website; source validation pending. Illustrative industrial footage: K and Luke Nomad / Pexels. Product images anonymized.
spiderAI™ is for you if any of these three is true.
The machine with no standby, the kiln, the compressor train. Speed of diagnosis converts directly to margin. Served live on a steelworks sinter line.
Failure stops nothing and bleeds silently: scrapped batches, energy fallback, guarantee shortfalls, product made worse and handed forward. Tools built around downtime never catch it. Delivered at a PVC compounding line.
Makes neither the product nor the margin, and its failure is not measured in tonnes. Water utilities sit under the same equipment intelligence as the production line, inside the live deployment today.
Every delivered project answers one question: have we stood inside an operation like yours.
Each was delivered with sensing, signal processing and machine learning, before large language models existed. The system found the fault and a person did the reasoning. spiderAI™ now does that reasoning, on ten years of industrial insight we found and confirmed.
Real results, in the words of the people who run the machines.
Before minto.ai's solution, we had 83 hours of production loss over three months, resulting in approximately 57,000 lost inserts. After implementing spidersense™, minto.ai's condition monitoring platform, 90% of this downtime was eliminated. Our maintenance engineers now have better awareness of machines and failure mechanisms, with some months seeing no downtime at all.
spidersense™ is the earlier minto.ai™ platform. spiderAI™ is built on it.
Kudos to the team at Minto Ai for identifying the problem accurately. Their timely detection helped us replace the mandrel assembly, preventing further damage caused by the smaller pulley key and keyway getting crushed.
After installing Minto Ai's system, we quickly identified an issue in the intermediate gearbox. Their system has been helpful in diagnosing the stalling motor and the gearbox shaft problem.
Building intelligence to run the world on time, efficiently and safely.
Every steelworks, fab, water network, data hall, mine, fleet and hospital runs on machines that already tell the truth about themselves, in a language nobody has had time to read. spiderAI™ reads it, remembers it and reasons across it, so the people who carry the consequences stop operating machines by guesswork and start governing the knowledge about them. The same people, doing the work only they can do, and nobody standing in front of an open machine wondering which of nine causes it is.
THE HUMAN MOVES FROM OPERATING THE MACHINE TO GOVERNING THE KNOWLEDGE ABOUT THE MACHINE.
Your operation already has the data.
What it is missing is the intelligence that turns it into the right decision, for the right person, at the right moment.