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AI training and consulting for manufacturing
Factories generate large volumes of data from machines, sensors and quality stations every day. We train your teams and take solutions to the shop floor with you, turning that data into quality, efficiency and maintenance decisions.
AI projects in manufacturing usually start on a single line with a single problem: catching defects earlier, reducing unplanned downtime or forecasting demand more accurately. The challenge is rarely building the model; it is running it reliably on the shop floor and scaling it.
That's why our manufacturing programmes address three things together: choosing the right use case, building data literacy in engineering and quality teams, and taking models to production sustainably.
Key AI use cases
Computer vision quality control
Detecting surface defects, missing parts and assembly errors from camera images.
Predictive maintenance
Spotting early signs of failure in sensor data to reduce unplanned downtime.
Demand and production planning
Demand forecasting, inventory optimisation and capacity planning.
Digital twins
Scenario analysis and process improvement with a virtual model of the production line.
Process and energy optimisation
Tuning process parameters, improving yield and energy consumption.
Knowledge access
GenAI assistants that search technical documents, maintenance manuals and procedures.
Regulation and risks
- EU AI Act: For manufacturers selling into the EU, AI used as a safety component of a product can be high-risk through the relevant EU product legislation. The Omnibus amendment postponed these product-based obligations to 2 August 2028.
- Intellectual property and trade secrets: Process parameters, recipes and design files should not go into general-purpose AI tools; use approved enterprise tools.
- Safety and human oversight: For models affecting shop-floor decisions (such as stopping a line), the final decision and responsibility should stay with a person, and model performance should be monitored regularly.
- Data protection: Camera and sensor data that can identify employees is personal data; any use for monitoring employees needs separate assessment.
Our experience: Quality control, predictive maintenance and digital twin solutions.
Recommended training
Programmes we recommend for the roles in your organisation. Every course is tailored with examples from your industry and delivered in person or online.
AI Strategy & Roadmap Workshop
A prioritised roadmap built from your own use cases with plant and function managers.
Learn more →Data Literacy & Analytical Thinking
Data-driven decisions and essential statistics for production, quality and planning teams.
Learn more →Machine Learning Without Code
Process and quality engineers building forecasting and classification models without code.
Learn more →Applied Machine Learning
Machine learning with sensor and production data for analytics teams.
Learn more →MLOps: Taking Models to Production
Monitoring, versioning and running models sustainably on the shop floor.
Learn more →Consulting and development services
Use Cases & Feasibility
We identify the highest-value use cases and validate them with a rapid proof of concept before you invest.
Learn more →Machine Learning Models
Models built for your business problems: scoring, risk, forecasting, segmentation and fraud detection.
Learn more →MLOps & LLMOps
A platform that automates training, versioning, deployment, monitoring and evaluation for both classic models and LLM applications.
Learn more →Integration & Data Pipelines
Integrating models and applications into your existing systems through APIs and reliable data pipelines.
Learn more →Free assessment · 4 min
Start now AI Maturity Assessment
Measure your organisation's AI maturity across 6 dimensions: strategy, data, technology, people, governance and culture.
Frequently Asked Questions
Where should manufacturers start with AI?
The healthiest start is a problem with measurable value, data that is ready, and a scope that can be tested on a single line, such as detecting a specific defect type with computer vision or predicting failures of a critical asset. A use case workshop structures this choice.
What data does predictive maintenance need?
Equipment sensor data (such as vibration, temperature and current), maintenance and failure records, and information on operating conditions. Well-kept failure records are one of the most critical conditions for success.
Do engineers need to learn to code for AI projects?
Not always. With no-code machine learning tools, process and quality engineers can build prototype models. For models connected to critical processes, working with data science and MLOps teams is recommended.
Why do AI models lose accuracy on the shop floor over time?
As raw materials, equipment, product mix or operating conditions change, the data changes too (data drift). Model performance should be monitored and models retrained when needed.
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Read more →Let's build a programme for your industry
Tell us about your needs, use cases and regulatory requirements, and we'll put together the right training and consulting plan.