Manufacturing
Quality, maintenance and planning AI on the shop floor, not in a slide deck.
The plant doesn't care about the demo. If the model can't read your historian, survive your shift patterns and beat the downtime number you already have, it isn't a deployment. It's a pilot with good lighting.
We build against production data, measure against your current process, and put 30% of our fee on the metric.
What we see most often
A predictive maintenance pilot that worked on the demo line and never survived contact with the plant's actual sensor data.
Quality inspection models whose false-reject rates quietly cost more than the defects they catch.
Planning and scheduling tools bought before the ERP and shop-floor data could feed them.
Vendor black boxes from equipment OEMs: no documentation, no audit right, no exit clause.
One line, one cell, one measurable number.
Defect rate, unplanned downtime, schedule adherence: pick one. A function pod with a written baseline beats a plant-wide programme with a slide deck. If the data isn't ready, the Diagnostic tells you exactly what fixing it costs before you commit to anything larger.
Questions we get asked
- Who does AI consulting for mid-size manufacturers in India?
- 72 Networks runs fixed-scope AI deployments for manufacturers between ₹200 crore and ₹5,000 crore in revenue: quality inspection, predictive maintenance and planning, in eight to twelve weeks with a measured baseline and 30% of the fee tied to the outcome.