STADLER Reshapes Knowledge Work at a 230-Year-Old Company

Context: Knowledge Bottlenecks in a Traditional Industrial Enterprise

STADLER, founded in 1794, is a global leader in rail vehicle and systems solutions, with deep technical documentation archives and highly fragmented expert knowledge. As engineers age and onboarding cycles lengthen, retrieval and reuse of unstructured documents—including PDF manuals, email threads, and meeting minutes—have become critical productivity bottlenecks.

Implementation: Lightweight ChatGPT Integration, Not In-House LLM Development

• No private Llama deployment or fine-tuning; instead, ChatGPT was embedded into internal SharePoint and Outlook via OpenAI API;
• All prompts underwent legal and information security review; sensitive engineering drawings and client contracts are explicitly prohibited from upload—only summary-level text is allowed;
• Pre-deployment ISO 27001 compliance assessment completed; all chat logs are stored locally and never transmitted back to OpenAI.

Key Outcomes and Quantitative Metrics

• Document summarization: Technical document reading time reduced from 47 minutes to 12 minutes per document (↓74%);
• Cross-departmental Q&A response: Median first-response time in internal knowledge base dropped from 4.6 hours to 1.2 hours (↑3.8×);
• Employee survey: 89% of 650 users engage with the tool ≥5 times weekly; average time saved per task is 1.2 hours/week—equivalent to ~13.8 FTEs (full-time equivalents);
• Zero new AI-dedicated roles; maintained entirely by existing IT support team + 3 internal prompt engineers.