Shop-floor networks can't talk to AWS. Models have to run at the edge, on industrial PCs, with strict OT-network isolation.
5-σ defects are the ones that matter. Datasets are imbalanced; the long tail is what kills naive vision models.
SAP ECC / S/4 HANA, Oracle EBS, IFS — every plant is a snowflake. AI that doesn't write back to ERP doesn't ship.
Sensor data is noisy, drift-prone, and covers thousands of asset variants. Generic models underperform; tuned ones earn their keep.
Trained on your defect set, deployed at the line on industrial PCs (NVIDIA Jetson / Intel NUC). Real-time pass/fail + reason codes.
Asset-tuned models on vibration, temperature, current. Drift monitoring. Failure-mode tagging tied back to your CMMS.
OEE, downtime, scrap, throughput — surfaced in dashboards that read SAP / Oracle / IFS natively, not via a parallel data swamp.
Multilingual copilots for line operators — error-code lookup, SOP retrieval, escalation drafts. Voice-first for noisy environments.
Edge-deployed vision QC on stamping line, 5-class defect classification. 99.94% precision after fine-tuning on plant data.
Asset-specific PdM models on motor + pump fleet. 37% reduction in unplanned downtime over 6 months.
Yes. Most manufacturing engagements are edge-deployed (Jetson / NUC / industrial PCs). The orchestration plane runs on-prem; no cloud dependency required.
We start with your existing pass/fail images + a 2-week capture sprint on the line. Active learning + targeted synthetic augmentation closes the long tail.
We've integrated with SAP ECC, S/4 HANA, Oracle EBS, IFS, and custom ERPs. Two-way sync via IDocs / BAPIs / REST adapters depending on your stack.