Background

Maintenance and service documentation for machinery and equipment — manuals, technical publications, SOPs — is increasingly authored in structured formats (e.g. DITA, S1000D, or manufacturer-specific XML/JSON) rather than free-form PDFs, since manufacturers need to manage and reuse content across many product variants and markets. Despite this, the documentation is still mostly delivered to technicians as static documents or basic search-based viewers, leaving them to manually navigate lengthy manuals to find the right procedure, parts list, or safety warning for the specific machine variant and task at hand. At the same time, generative UI capabilities are maturing to a point where well-structured, tagged content could be turned into interactive, task-specific applications rather than static documents — an opportunity that has not yet been systematically applied to maintenance work.

Challenge

How can structured maintenance documentation be turned automatically into a dynamic, task-specific maintenance app, rather than requiring technicians to search through static manuals? In this project the team can choose to focus on any device or machine where they can find extensive-enough documentation (for example; dishwashers, bikes, air conditioning). The project will investigate:

• Content structuring & parsing: how to reliably parse standards-based structured documentation (e.g. DITA, S1000D, or manufacturer-specific XML/JSON) into a machine-usable model of procedures, steps, parts, warnings, and variants.
• Dynamic app generation: how AI can generate a task- and machine-variant-specific maintenance app or interface (step-by-step guided flow, checklists, part lookups) on demand from the structured content, instead of a one-size-fits-all manual viewer.
• Personalization & context awareness: how the generated app can adapt to the specific machine variant, fault code, or maintenance task at hand, and to the technician's skill level.

Goal

1. Build a pipeline that parses structured maintenance documentation into a machine-usable content model.
2. Generate a dynamic, task-specific maintenance app or interface from that model for a given machine variant and maintenance task.
3. Test how the generated app can adapt to different machine variants.

Scope

The project is not aimed at building a production authoring and publishing platform, but at validating whether structured documentation can be reliably transformed into a working dynamic maintenance app. The scope is limited to one or two representative pieces of equipment or machine types with a simplified but realistic set of structured documentation, so most effort goes into testing the parsing, generation, and safety-validation approach rather than building a fully-featured product.