Your Best Engineer Is About to Retire. What Happens to Everything They Know?
We build AI agents that capture decades of operational expertise and make it available to every operator, every shift, every day. Not generic chatbots. Purpose-built intelligence trained on your machines, your documentation, and your data — augmenting your team instead of replacing it.
Your Data Is Growing. Your Ability to Use It Isn’t.
Three layers of pain that build on each other: knowledge trapped in people, data trapped in systems, and the ChatGPT disappointment. The visitor should feel understood — and ready for an alternative.
The knowledge problem
Your maintenance lead knows that Machine 7’s vibration pattern changes two days before a bearing failure. Your senior quality inspector can spot a surface defect by sound alone. Your production planner knows which job sequences minimize changeover time — not because it’s written down, but because they’ve been doing it for 25 years.
This expertise isn’t in any system. It lives in people’s heads. And when those people retire, take sick leave, or change roles, the knowledge walks out the door. The new hire has the degree but not the instinct. The temp knows the process but not the exceptions. And nobody wrote it down because nobody had time.
The data problem
Meanwhile, your machines generate terabytes of sensor data. Your ERP holds years of production records. Your quality system has thousands of inspection reports. Your maintenance logs describe every repair. But all of this sits in disconnected databases, PDF files, and folders nobody opens. You have more data than ever and less insight than you need.
The ChatGPT disappointment
So someone on your team tried ChatGPT. It wrote a decent email. It summarized a document. But when they asked it about your specific machine’s failure patterns, it hallucinated an answer that sounded plausible and was completely wrong. Because generic AI doesn’t know your machines. It doesn’t know your maintenance history. It hasn’t read your 400-page equipment manual in German. And it certainly doesn’t understand OPC-UA telemetry from a HOMAG CNC router.
The symptoms we hear most:
“We’re losing people faster than we can train replacements.”
Institutional knowledge is evaporating with every retirement.
“We have the data but nobody has time to analyze it.”
Insights are locked inside systems that require specialist skills to query.
“We tried AI tools and they don’t understand our domain.”
Generic LLMs produce confident-sounding nonsense about your specific equipment.
“Our reporting takes days when it should take seconds.”
Shift summaries, compliance reports, and quality dashboards are assembled by hand.
“We can’t scale what our best people know.”
One expert’s skill can’t be cloned across three shifts and two plants.
First, We Find Where AI Will Actually Work
Not every process benefits from AI. Some are better solved with integration, automation, or straightforward engineering. The Analyze phase identifies where AI agents will deliver real ROI — and where they won’t. This saves you from the most expensive mistake in enterprise AI: building something impressive that nobody uses.
What our AI specialists do in the Analyze phase:
AI opportunity scoring (in Discovery)
We evaluate every process and data source for AI applicability. Where is unstructured data being underused? Where are decisions bottlenecked by human availability? Where would a predictive model change behavior? Each opportunity is scored by impact, feasibility, and data readiness.
Prototyping / AI POC (5 weeks)
For the highest-scored opportunity, we build a working prototype using your real data. Not a canned demo with sample data — a functional agent tested against your actual production history, maintenance logs, or quality records. You see it work (or not) before committing to a full Build.
What you walk away with:
AI Opportunity Map
Every AI-applicable process ranked by impact, feasibility, and data readiness. Clear identification of quick wins vs. strategic bets.
Discovery: €15–25k Prototyping: scoped per opportunity
Working Prototype
A functional AI agent or model tested on your data. Performance metrics. A demo environment your leadership can evaluate firsthand.
Discovery: 4 weeks Prototyping: 5 weeks
Production Business Case
Projected ROI. Technical scaling plan. Resource requirements. Go/no-go recommendation with evidence, not assumptions.
We use your real data, not sample datasets. You see AI work on your actual machines and processes before any large investment.
Then, We Build Intelligence Into Your Operations
AI Agents is the primary discipline in 10 Build services — more than any other Area except Engineering. Where Engineering provides the infrastructure, AI Agents provides the intelligence. These two disciplines work in tandem across nearly every Cadran project.
AI That Gets Smarter the Longer It Runs
An AI model deployed today will degrade tomorrow unless it’s actively managed. Production data changes. Machine behavior drifts. New products are introduced. Regulations evolve. The Operate phase ensures your AI agents keep performing — and keep improving.
MLOps & Scalability
Continuous model monitoring, automated retraining triggers, performance dashboards. When your predictive maintenance model starts drifting because you’ve added a new machine type, we detect it and retrain before accuracy drops. Infrastructure scales automatically as data volumes grow.
AI Security & Risk Mitigation
EU AI Act compliance for all deployed models. Data governance for sensitive production data. Adversarial robustness testing. Bias monitoring. Explainability documentation that satisfies both regulators and your leadership team.
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Continuous Improvement
Quarterly reviews of agent performance against business KPIs. New training data integration. Feature expansion based on user feedback. Your AI agents don’t stagnate — they compound in value over time.
The critical difference: most AI vendors deploy a model and move on. We stay because our fees are tied to your outcomes. If the model stops delivering, we feel it too.
The AI Stack Behind Our Agents
We’re model-agnostic and platform-agnostic. We choose the right foundation model, vector database, and orchestration framework for each use case — and we switch when something better emerges. What doesn’t change: our commitment to safety, explainability, and production-grade reliability.
Cadran is a member of the Anthropic Claude Partner Network — a select ecosystem of partners with access to advanced Claude capabilities, technical certification, and co-development resources. This means our AI agents benefit from Claude’s industry-leading reasoning, safety alignment, and enterprise-grade reliability. For clients in regulated industries (manufacturing compliance, energy grid operations), Claude’s emphasis on safety and accuracy is a critical advantage over alternatives.
Case studies
Strategic Partnership
Since mid-2022, Cadran has been the strategic technology partner for HOMAG Group — the world’s leading provider of integrated solutions for woodworking production. We co-develop and maintain materialManager and materialManager Advanced, HOMAG’s web-based applications for centralized material data management, inventory control, and automated allocation across edgebanding and panel processing operations.
What we built together: seamless data import from Excel, ERP systems, and industry software; real-time inventory tracking with automated material allocation and booking; direct integration with HOMAG machines and production planning software to optimize cutting and nesting; and automatic saw parameter adjustment based on materials, tools, and quality requirements. The result: automated material allocation and optimized cutting reduced waste by up to 15%, while real-time inventory tracking eliminated manual errors and improved operational accuracy.
Homag
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Cadran co-develops Ceiton’s enterprise workflow and resource management platform, serving broadcasters from MDR to Red Bull Media House with scalable project management and scheduling automation.
Ceiton Technologies — CEITON Workflow & Resource Management Platform
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Who Builds Your AI Agents
AI Solutions Architect
Designs the AI system architecture. Selects the right models, frameworks, and deployment patterns for your use case. Leads the AI opportunity assessment in Discovery and the technical design in Build.
ML / AI Engineers
Build, train, fine-tune, and deploy the models. Specialists in predictive maintenance ML, computer vision for quality inspection, NLP for document processing, and LLM-based agent development. Fluent in both PyTorch and production deployment.
Prompt Engineers
Design the conversational architecture, prompt chains, and guardrails for LLM-based agents. Ensure agents answer accurately, stay within their domain, and handle edge cases gracefully. Critical for RAG systems and knowledge base assistants.
Data Engineers
Prepare, clean, and pipeline the data that feeds your AI agents. Build the RAG retrieval layer. Ensure data flows from your machines and systems into the models reliably and at scale. (Cross-disciplinary with the Engineering team.)
MLOps Engineers
Deploy models to production. Build monitoring and retraining infrastructure. Ensure your AI agents perform at launch and improve over time. Bridge the gap between “it works in a notebook” and “it works at 3 AM on shift 3.”
Cross-disciplinary by design.
AI agent projects always involve Engineering (for the infrastructure) and often Integration (for data connectivity). Our team structure reflects this — specialists collaborate across disciplines rather than working in silos.
Questions We Get Asked About AI Agents
Valid concern. Generic LLMs do hallucinate when asked about domains they weren’t trained on. Our approach mitigates this in three ways: (1) RAG architectures that ground every answer in your actual documentation and data — the agent cites its sources; (2) guardrails that prevent the agent from answering questions outside its trained domain — it says “I don’t know” instead of guessing; (3) human-in-the-loop design for high-stakes decisions — the agent recommends, a human approves. We also use Anthropic Claude, which is specifically designed for safety and accuracy in enterprise contexts.
Your data never leaves your control. We offer three deployment models: (1) cloud-hosted within EU data centers (Azure Germany, Hetzner) for full GDPR compliance; (2) hybrid deployment where sensitive OT data stays on-premise and only aggregated insights go to the cloud; (3) fully on-premise for the most security-conscious environments. We also support Mistral AI as an EU-sovereign model option for clients who require European data residency end-to-end. All deployments include data governance frameworks aligned with GDPR, EU AI Act, and where applicable, NIS2.
ChatGPT is a general-purpose assistant. It’s excellent at writing emails and summarizing articles. It’s terrible at answering questions about your specific CNC machine’s maintenance history, because it’s never seen your data. Our AI agents are purpose-built: trained on your documentation, connected to your machine data via RAG, and constrained to your domain. They answer questions like “What caused the last three stoppages on Line 4?” by querying your actual maintenance database — not by generating a plausible guess.
That’s precisely why we exist. Our AI team does the development, and our Operate phase (MLOps) handles ongoing management. Your team doesn’t need to train models, tune hyperparameters, or monitor drift. They interact with the AI agent through a natural interface — asking questions, reviewing recommendations, and approving actions. The complexity is hidden behind simplicity.
Only if we design it right. We build AI agents into the tools your operators already use — not as a separate app they’ll never open. A maintenance copilot appears inside your CMMS. A quality agent surfaces alerts in the dashboard your QC team already watches. The interface is in German. The training is hands-on. And our Operate phase includes Change Management specifically to ensure adoption. If the operators don’t use it, the project has failed — and our outcome-tied fees mean we feel that failure too.
AI systems used in industrial safety and critical infrastructure fall under specific EU AI Act requirements. Our AI Security & Risk Mitigation service in the Operate phase covers: risk classification of your deployed AI systems, documentation and transparency requirements, human oversight mechanisms, and ongoing monitoring for bias and drift. We’re building EU AI Act compliance into every agent from day one — not retrofitting it later.
Ready to see what your data can do?
Start with a conversation. No pitch deck. No obligation. Just a focused discussion about your operations and where AI could make the biggest difference.