AI automation that does the work software should be doing — for Manufacturing

Manufacturing in DACH and Europe more broadly is dominated by mid-market specialists — the Hidden Champions, the Mittelstand machine builders, the contract manufacturers, the Tier-1 and Tier-2 automotive suppliers. These businesses are extraordinarily good at their physical product and consistently undersupplied on software. ERPs are old (SAP ECC, Navision, ProAlpha), MES systems are partial or absent, customer-facing tooling barely exists, and CRMs are spreadsheets in 60% of cases. The decision-makers are pragmatic and ROI-driven — a manufacturer will sign for a piece of software the day they can see what it will save on the shop floor, and not before. Long-term partnerships matter; vendor-hopping is rare.

• ▸Demand-forecasting models that incorporate dealer feedback, market signals, and seasonality — typically 15–25% better than ERP-default forecasts.
• ▸Quality-defect classification from production-line imagery — strong ROI in continuous-process manufacturing (textiles, paper, food).
• ▸RFQ response automation — extracting specs from incoming customer enquiries (PDFs, Excel) and pre-filling internal quoting tools.
• ▸Predictive maintenance on machinery is a real ROI story when sensor data already exists — pointless when it doesn't.
• ▸Customer-service automation for the 60% of tickets that are "where is my order" — saves CS hours, raises NPS, low regulatory exposure.
• ▸Start where the data is clean — most manufacturing AI projects fail not on the model but on the data lineage and master-data quality.

For a Tier-2 automotive supplier, we built an RFQ classification and pre-quoting agent that cut new-customer-enquiry response time from 4 days to under 8 hours — a 91% reduction with no headcount change.