AI lowers throughput time and defect rate in manufacturing
Quality assurance, maintenance, and technical documentation are the functions where AI agents have the biggest effect at manufacturing companies.
AI levers in manufacturing
Manufacturing companies work with ERP and MES landscapes that grew over time, a lot of implicit knowledge held by experienced staff, and processes optimized over decades but rarely documented digitally. Quality problems are often caught late, maintenance reacts instead of predicting, and technical documentation for customers and internal teams is created manually. Every one of these gaps costs throughput time or raises the defect rate.
We apply AI where data is already captured but not used: machine and sensor data, inspection records, service histories. Agents analyze this data, flag anomalies for people, and answer recurring technical questions from existing documentation. The goal isn't to automate production processes that already run well, but to speed up the information flows around them that cost time today.
Functions with the largest lever
- 1
Quality assurance
Agents analyze inspection data and flag deviations earlier than manual spot checks can
- 2
Customer service
An agent with access to product and service history answers technical customer questions directly
- 3
Marketing & content
Technical documentation and product data sheets come from existing design and inspection data
- 4
Finance & reporting
Automated analysis of production and scrap costs by line and batch
- 5
Software development
Agentic Engineering speeds up integrations between ERP, MES, and new digital services
→ Throughput time
Typical use cases
Quality analysis from inspection data
An agent reads inspection records and sensor data and flags deviations before they turn into scrap or claims.
Technical service agent
Answers customer questions on spare parts, maintenance intervals, and error codes from existing service documentation.
Automated technical documentation
Generates data sheets and manuals from design and inspection data, editorially reviewed before release.
Predictive maintenance
Analysis of machine data to prioritize maintenance work instead of fixed maintenance intervals.
ERP–MES integration with Agentic Engineering
Faster connection of new equipment and digital services to existing systems through AI-assisted development.
Prerequisites
| Area | Requirement | Typical state |
|---|---|---|
| Data | Machine, sensor, and inspection data in an analyzable form, not just on paper or in isolated systems | Varies; newer equipment delivers data, older equipment needs retrofitting or manual capture |
| Systems | ERP and MES with interfaces an agent can read and, within limits, write to | Usually in place, often grown historically with poorly documented interfaces |
| Organization | Quality and production leads jointly define which deviations an agent should report | Often not formalized; knowledge sits with individual experienced staff |
Requirement: Machine, sensor, and inspection data in an analyzable form, not just on paper or in isolated systems
Typical state: Varies; newer equipment delivers data, older equipment needs retrofitting or manual capture
Requirement: ERP and MES with interfaces an agent can read and, within limits, write to
Typical state: Usually in place, often grown historically with poorly documented interfaces
Requirement: Quality and production leads jointly define which deviations an agent should report
Typical state: Often not formalized; knowledge sits with individual experienced staff
Our approach in manufacturing
We start with an assessment of what data ERP, MES, and the machine level already capture and where it goes unused. That produces a prioritized roadmap starting with the use case with the clearest effect on defect rate or throughput time. We build the first agent together with quality or service technicians, because their experience supplies the rules an agent can't derive on its own at first.
| Ausgangslage | Umsetzung | Ergebnis | |
|---|---|---|---|
| D2C food brand, Bremen | D2C food brand without permanent technical leadership, open shop and ERP decisions. | Shop evaluation, new architecture, and a guided ERP migration as interim CTO. | Decisions were made and implemented, no longer postponed. |
| EdTech company, engineering organization | Engineering team used AI coding tools individually, without a shared workflow. | Toolchain, adapted workflow, and quality gates for AI-assisted development. | The team works agentically as the default, with controls that fit the new way of working. |
Ausgangslage
D2C food brand without permanent technical leadership, open shop and ERP decisions.
Umsetzung
Shop evaluation, new architecture, and a guided ERP migration as interim CTO.
Ergebnis
Decisions were made and implemented, no longer postponed.
EdTech company, engineering organization
Ausgangslage
Engineering team used AI coding tools individually, without a shared workflow.
Umsetzung
Toolchain, adapted workflow, and quality gates for AI-assisted development.
Ergebnis
The team works agentically as the default, with controls that fit the new way of working.
Frequently asked questions
Initial call: 30 minutes, concrete.
We look at your quality and service data and tell you where an agent saves throughput time.