For battery, automotive & advanced manufacturing

Catch the defect before the line does.

IIoT sensors, MES records, microscope images, lab results, manuals — fused into AI that predicts quality, machine health and yield, and lets engineers ask the plant in plain language.

~$5M
Quarterly savings from contamination detection on a battery line
4 wks → 10 min
Root-cause analysis time at a semiconductor fab
200 hrs/wk
Reclaimed by moving IoT analytics from batch to real time
Mine → Car
Battery-life models spanning raw material to vehicle
Product · VitalFusion Edge

One governed model of the plant, served over i3X.

PLC tags, MES events and vision detections modeled once — then priced: every production-loss event reconstructed, costed, and answered with the cheapest countermeasure, simulated first.

One plant modeli3XProduction-Loss IntelligenceChat with your factory
Explore VitalFusion Edge →
What we build

Industrial AI that earns its place on the floor.

Every model below runs in a real plant — on streaming data, under load, in front of the people who act on it.

Contamination detection

Vision on SEM and optical images finds foreign material and predicts its effect on battery life.

Battery-life & yield prediction

Process, chemistry and imaging data fused to forecast cell life, yield and new-material performance.

Machine health & maintenance

Asset-to-asset health on live streams — failures and line starvation caught before they cost a shift.

Chat with your plant

Plain-language answers over live data, manuals and drawings — with the source behind each.

Real-time digital twins

Batch pipelines turned into live twins that keep asset relationships current.

Scheduling & operations research

Optimization for job scheduling, yield and line starvation under real constraints.

Signature capability

From microscope image to a dollar figure on battery life.

A fleck of metal invisible to the eye can shorten cell life and trigger recalls. We built the pipeline that finds it early.

Vision models flag particles in SEM captures; a predictive layer ties each one to its likely effect on cell life using process and chemistry data. Four-week investigations became near-real-time detection — about $5M a quarter.

contamination-detector · live
InputSEM capture · cell #A2241-07 · coating step
Detect2 metallic particles · 18µm, 24µm · confidence 0.96
PredictEst. cycle-life impact: −9% · flag for quarantine
ActAlert line supervisor · log to MES quality lineage

Illustrative detection flow

Where it pays off

From the plant floor to the enterprise.

01

Quality & contamination

Contamination detected and quality predicted in real time — before defective product moves downstream.

02

Traceability & root cause

Sensor data joined to MES for product and process lineage — root cause in minutes, not weeks.

03

Predictive maintenance

Live asset relationships that anticipate failures and prevent unplanned downtime.

04

Knowledge at the operator's hand

Manuals, videos and research queried in plain language, without leaving the floor.

"We built AI/ML models spanning mine to car — fusing IoT data from every processing step, raw-material chemistry, and electron-microscope imaging to predict battery life and the impact of contamination."

— Engagement summary, leading battery manufacturer (anonymized)
Global Battery Manufacturer Tier-1 Automotive OEM Semiconductor Fab Class-I Railroad Life-Sciences Manufacturer
Industrial Intelligence

Point us at a defect you can't explain.

A focused proof-of-concept on your line's real sensor, image and MES data — inside your environment, scored against ground truth.