About Brantner Solutions

Brantner Solutions is a manufacturing technology consultancy based in Eau Claire, Wisconsin. We build the data pipelines, analytics tools, and integrations that small and mid-sized plants need but can't justify a six-figure consulting engagement to get.

Every engagement is a fixed-price project. We deliver working tools, not slide decks. Your team gets trained, your systems run on your infrastructure, and there's no vendor lock-in or recurring fees. We work fast because we've been on the other side of the table, waiting for a vendor to deliver something useful.

Founder

Chris Brantner

Eau Claire, WI

Nine years in manufacturing across quality, production, maintenance, and distribution. Contributed to rapid continuous improvement events that cut scrap 36%. Piloted real-time SPC alarm systems directly on the plant floor. Calibrated gauges, troubleshot film defects, and trained operators on process control.

B.Sc. in Applied Mathematics and Statistics, currently completing an M.S. in Data Science, both from UW-Eau Claire. Has built a surface defect classifier that hit 95% accuracy across six defect classes, adaptive predictive maintenance models benchmarked on real bearing datasets, and a published R package on CRAN for DOE visualization. Presented on algebraic codes at the Joint Mathematics Meetings.

github.com/cjbrant

Selected Projects

Surface Defect Classifier

Fine-tuned ResNet-18 on steel surface defects. 95% accuracy across 6 defect classes. Includes an interactive dashboard with defect pattern analysis, cost tracking, and machine-level breakdowns for plant leadership.

Adaptive Predictive Maintenance

PID adaptive control for bearing remaining useful life prediction. Benchmarked across 5 real-world datasets from 3 manufacturers.

AI + MFG

AI and Manufacturing Reshoring

Webpage essay analyzing the cost effects of AI on U.S. manufacturing and its impact on reshoring labor.

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Adaptive Drift Forecasting

Control-theoretic time series forecasting using PID, sliding mode, and L1 adaptive controllers for nonstationary process data.

ixsurface (CRAN)

Published R package on CRAN for 3D visualization of interaction effects from designed experiments. Built for process engineers optimizing multiple factors.

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