Every machine on your floor emits vibration, current draw, cycle time and thermal drift. Almost all of it is discarded within seconds of being produced.
tan.ai keeps it. It learns what normal looks like for your specific equipment — not a textbook average — and tells you when something is drifting away from it. That is the difference between a planned stop on Friday and a line down on Tuesday.
It grew directly out of our industrial IoT practice. Once a reliable pipeline exists off a machine, the interesting question stops being what is it doing and becomes what is it about to do.
A digital twin of the cell, fed by the same sensors as the machine, running alongside the real thing.
Bearing wear, spindle imbalance and tool degradation carry a changing vibration and thermal signature for weeks before anything fails. Models trained on your own equipment learn what normal sounds like, then raise a hand while it is still a maintenance job rather than a stoppage.
Camera-based surface and dimensional checks running at cycle speed. It catches porosity, weld spatter, burrs and finish defects that a tired eye misses on the third shift, and keeps a labelled image record for every part you ship.
Give it your load cases, material and manufacturing constraints. It returns geometry that hits the stiffness target at lower mass, and stays inside what your machines can actually cut — no shapes you cannot make.
A live model of the cell running alongside the real one, fed by the same sensors. Test a tool path, a fixture change or a new takt time against real production data before anyone touches the floor.
Cycle time, scrap rate and energy per part tuned together rather than one at a time. The model proposes parameter changes and shows you the trade-off before you commit to anything.
Dimensions, GD&T callouts and material specifications pulled out of legacy 2D prints and scanned drawings, then pushed into your PLM as structured data instead of being retyped by hand.
Nothing here asks you to rip out a control system or commit to a platform before you have seen it work.
Two weeks. We look at what your machines already emit, what is being stored, and which decision would actually change if somebody could see it. You get a written finding either way — including "not yet worth it" if that is the honest answer.
One cell, one question, one agreed measure of success. We instrument it, establish a baseline of normal operation, and run for a defined period. Nothing connects to production control during a pilot.
If the pilot holds up, we extend across the line or the site. Models retrain on your data as it accumulates, and the alert thresholds stay yours to adjust.
Your team runs it day to day. We keep the models honest as equipment ages and the product mix changes, because a model trained on last year's process quietly stops being right.
tan.ai does not predict failures it has never seen an example of. It needs a period of normal operation to learn from, and genuinely rare failure modes stay rare.
What it reliably catches is drift — the slow departure from normal that precedes most mechanical failures and nearly every quality escape.
And if the real problem is that nobody acts on the alerts you already have, more alerts will not fix it. We will say so during the assessment rather than after the invoice.
Two weeks, one written finding, no obligation to continue.
Send a drawing, a problem statement, or a rough idea. An engineer replies — not an autoresponder.