Industry · Manufacturing
Private AI for the production line.
Manufacturing is where data was first operational. Every shift produces signals — work-order timestamps, quality inspection results, machine state. We help plants and packaging operations turn that signal into a private AI system that runs on your floor, on your data, with no vendor lock-in.
Industry Challenges
Why most AI pilots never reach the line.
- Data scattered across ERPs and spreadsheets
Critical signals live in legacy systems that do not talk to each other. Models starve before they start.
- Defects caught too late
End-of-line inspection flags failures only after hundreds of units have already shipped the same root cause.
- Planning expertise concentrated in two people
When the senior planner is on vacation, the schedule quality drops 30-40%. That is a knowledge risk, not a tooling gap.
- Changeover setup eats the day
Setup time on multi-SKU lines is often longer than the run itself. Each shift is leaving throughput on the table.
What We Deliver
Production-grade AI capabilities, end to end.
How it works in practice
From the shop floor to the schedule.
From machine telemetry to shift decisions
We start by reading the signals the line already produces — spindle current, vibration, cycle-time, scrap rates, operator inputs. The first model sits on top of that stream and learns the parameter combinations that historically preceded a defect. By the second month we are typically catching the precursors to bad output at 50 units, not 500. The same telemetry, framed as a clustering problem, becomes a changeover recommendation engine; framed as a regression problem, it becomes a labor-time predictor that the planner can lean on.
Edge runtime on a small GPU-capable box next to the machine. Inference results stream into the MES, not into a dashboard no one opens.
The training-data moat
Every model we train is private to the engagement. Your 14 months of historical work orders, your 8,000 finished batches, your 18 months of MES exports — that corpus never leaves your environment. The model trained on it gets smarter with every new batch, while remaining structurally impossible for a competitor to reproduce. The compounding return is what turns a private-AI deployment from a one-off build into a moat that widens every quarter.
Monthly retraining on the rolling 90-day window. Drift alerts when the process changes. Quarterly reviews of which model outputs are still being acted on.
Knowledge capture as a deliverable
The hidden KPI on a manufacturing AI engagement is new-hire ramp-up time. When the model encodes the pattern catalog of your senior operators, an eight-week onboarding replaces a six-month one. We treat that metric as a first-class outcome — measured at month three, six, and twelve — and we walk away from model outputs that the floor is not using, even if those outputs are technically accurate.
Per-cohort comparison of new-hire vs. senior operator performance, surfaced in the monthly review.
Selected Work
What this looks like in production.
Recent Writing
Long-form notes from the floor.
How We Cut a Packaging Plant's Planning Time by 50% with a Custom AI Model
A 300M RMB/year packaging manufacturer was running its entire schedule from two planners' heads. We built a private AI model on their historical work orders and cut planning time in half while lifting on-time delivery from 78% to 94%.
Read articleAI Defect Detection and Changeover Optimization on the Shop Floor
In a high-mix hardware processing shop, AI defect detection and changeover optimization cut setup time 18% and new-hire ramp-up 67% — without giving up the data. Here is the private-AI playbook we shipped.
Read articleWorking on a manufacturing AI problem?
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