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AI Automation · for Manufacturing

AI Automation for Manufacturing teams — built for the way you actually work.

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.

Book a 30-min call AI Automation overview

EU hosting · Audit trails · Human-in-the-loop · DACH-native

What we see in Manufacturing today

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.

Regulatory context: ISO 9001 documentation expectations, traceability requirements in regulated verticals (medical devices, food, aerospace), CSRD reporting from 2024 onward for large companies. Less regulated than insurance or healthcare overall.

Pain points we hear from managing directors

  • Sales reps and dealers wait days for current order status because ERP queries are slow or locked-down
  • Production planning is still partially manual — Excel rolls forward weekly, reconciled against ERP afterwards
  • No clean view of customer profitability across business units — each unit reports separately
  • After-sales service tickets live in email and a shared mailbox, not a structured system
  • Quality data exists but is locked in MES exports — engineering needs to ask for CSVs
  • Customer portals are basic (PDF downloads + an order form) when buyers now expect Amazon-grade self-service

What AI automation looks like for a manufacturer

Decision-makers: Managing Director, Head of Operations, Head of IT, Head of Sales Ops, Production Manager. Here is what we focus on when ai automation meets manufacturing:

  • 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.

Proof: how we have done this for a manufacturer

Case study

From three tools and a shared inbox to a single internal CRM — in five weeks

Replaced three legacy tools with one custom CRM and cut the quote cycle by 40%

  • 5 weeks

    From kickoff to UAT

  • 3

    Legacy tools retired

  • 40%

    Faster quote cycle

Read the full case study →

Common questions

AI Automation for Manufacturing: what teams ask first

Is AI automation realistic for a Mittelstand manufacturer with a 20-year-old ERP?

Yes, but the order matters. Most manufacturers should not start with the model — they should start with a 2–3 week data audit. If your ERP can produce clean exports of orders, production runs, customer master data, and quality results, you're 70% of the way to useful AI. If the exports require manual cleanup every time, fix that first. We typically pair an AI pilot with a small ETL/data-platform investment because the ETL pays for itself even if the AI pilot is unsuccessful — meaning you de-risk the bigger story.

How do you typically scope a ai automation engagement for a manufacturer?

We start with a 30-minute discovery call to understand your specific situation. If there's a fit, we follow with a 1–2 week scoping engagement that produces a fixed-scope, fixed-timeline proposal. No hourly-billing surprises after the contract is signed.

Do you work remotely or on-site?

Primarily remote — most of our manufacturing clients are spread across multiple offices anyway. We come on-site for kick-offs and major workshops when it adds clear value (typically 2–4 visits across an engagement). EU-based team, EU working hours.

Related — explore other combinations

Ready to talk about ai automation for your manufacturing business?

30-minute discovery call. No sales theatre. If we're not the right fit, we'll say so.

Book a discovery call AI Automation overview

EU hosting · Audit trails · Human-in-the-loop · DACH-native