[ Industry case study — AI & Automation ]
Multilingual RAG platform puts Linde's knowledge seconds away
STX Next built a secure, Azure-hosted RAG platform that lets Linde employees query internal documentation in natural language and get source-cited answers in seconds.
Original project by STX Next ↗Industry reference — not a NexSum Labs engagement.
- Client
- Linde
- Industry
- Industrial gases & engineering
- Services
- Retrieval-augmented generation, AI/ML engineering, Data engineering, Cloud infrastructure
- Year
- 2024

The client
Linde is a leading global industrial gases and engineering company with a large international workforce. It holds a vast library of internal technical, policy and operational documentation written in many languages.
The challenge
Information on equipment specifications, safety protocols, maintenance procedures and internal policies was scattered across PDFs, scanned files, tables and multilingual content. Finding accurate answers was slow, error-prone and meant searching through several systems. Linde wanted a simple, secure way for employees to chat with the company knowledge base in natural language, wherever they are.
The solution
01Custom RAG pipeline
User questions first retrieve the most relevant passages from Linde's document store, and an LLM then generates a contextual answer grounded in those passages.
02Data sovereignty on Azure
The whole solution is hosted on Microsoft Azure inside Linde's own infrastructure, with no external data transfers.
03Multilingual document handling
Advanced text splitting, search-friendly metadata, separate table handling and automated sync pipelines keep a mixed corpus of PDFs, scans and tables indexed and current.
04Citations and quality monitoring
Every answer cites the exact documents and passages it came from, and a monitoring framework continuously tracks chatbot reliability.



The results
Employees now get accurate answers in seconds instead of digging through shared drives or asking colleagues. Cross-language retrieval opened knowledge to staff in every region regardless of the source document's language. Source citations and in-house hosting increased trust, and automated indexing keeps the knowledge base maintainable as documentation grows.
Tools & stack
Original project by STX Next ↗ — published by the delivering agency, included here as an industry reference.
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