The line that
never surprises you.
We put AI on the factory floor, in the maintenance schedule and inside the quality lab — turning unplanned downtime, scrap and rework into numbers that keep shrinking, quarter after quarter.
Downtime and defects
are still winning.
One hour of a stopped line can erase a day of profit — and preventive maintenance schedules still miss the failures that matter most.
Sensor telemetry, vibration and thermal signal fused with maintenance history — models predict the specific asset, the specific mode of failure, and the hours remaining.
Human QC catches obvious defects, misses subtle ones, and cannot inspect every unit at line speed on modern SKUs.
Vision AI inspects every unit, at every station, at line rate — with defect classification, root-cause tracing and closed-loop feedback to the process.
Demand swings, supplier variability and OEE drift make static schedules a source of daily firefighting.
Optimization models rebalance the plan continuously — respecting real constraints, real supplier ETAs and real machine state.
From a stubborn business problem
to a number on the board.
A single unplanned stop can cost a full shift of throughput.
Vibration, thermal, torque and history fused into a live health model.
Work orders opened, parts staged, technicians dispatched — automatically.
OEE up. Overtime down. A shift ends the way the plan said it would.
What changes when the line
reads its own future.
Measured across live deployments in automotive, industrial equipment, consumer packaged goods and semiconductor — reported by the plants themselves.
What it looks like
on the ground.
Predictive maintenance
Sensor streams from every rotating asset flow into a health model that names the failure mode, the affected component and the shift-window in which to act.
Forty-seven percent less unplanned downtime — in ten months.
We instrumented every asset on the line, fused the signal with two decades of maintenance history, and put the model in the hands of the technicians. The line stopped surprising them.
Chosen by operators who count in cycle time.
Every engagement is co-led by ex-plant managers, controls engineers and reliability leads. The team knows what a torque spike sounds like.
IEC 62443-aligned, air-gap capable, deterministic latency. The models never destabilize the network they run on.
Edge and on-prem deployment options. Your process data, telemetry and CAD stay where they already live — inference happens next to the asset.
SAP, Siemens, Rockwell, PTC, Aveva — all supported through native connectors. Existing MES, historian and CMMS investments are preserved.
We operate the platform with your reliability and quality teams for the life of the program — retraining, tuning, extending — not walking away at go-live.
Straight answers,
before the pitch.
The platform respects the Purdue reference model. Edge inference nodes sit at Level 2/3, telemetry aggregation at Level 3.5, and enterprise analytics at Level 4 — with unidirectional data diodes where required by your architecture.
No. Most programs start on the highest-cost failure modes with existing sensor coverage — and expand as ROI compounds. We do not require a green-field instrumentation project to see impact.
Native connectors exist for every major PLC, DCS and historian generation still in production service — including Rockwell ControlLogix, Siemens S7, Emerson DeltaV, OSIsoft PI and Aveva Wonderware.
Cameras and edge inference nodes are commissioned station-by-station. Defect classifications close the loop back to the process — either as operator alerts or, where appropriate, as automated setpoint adjustments.
Standard CBM tells you an asset is running hot. Our models tell you which asset, which failure mode, and which shift — because they reason across vibration, thermal, torque, oil condition and maintenance history together.
Take the surprise
out of the line.
A single conversation with our manufacturing practice. No slides. No obligation. Just the frank read on where AI would earn its keep in your business.