In many manufacturing companies technical knowledge exists, but it’s spread across PDFs, paper revisions and people who are about to retire.
None of these three conditions is a problem as long as the system holds. It holds because there are five or six people who know where to look and, more often, know the answer without looking.
The problem shows up when those people aren’t there: on vacation, on a business trip, retired. That’s when it becomes visible how much operational continuity depends on knowledge that was never formalized.
The invisible cost of inaccessible knowledge
It’s invisible because it doesn’t show up on any income statement. It’s spread across short, frequent episodes.
A field service technician calling headquarters for a spec that’s written in a manual he can’t find.
An operator waiting for confirmation on a tolerance before proceeding.
An estimator manually rebuilding a configuration already quoted two years earlier, because finding it again takes longer than redoing it.
A new hire who takes months to become self-sufficient, not because the work is hard, but because the information is scattered.
A job carried out on a superseded revision of the manual, with the rework that follows.
Taken individually, these are minutes. Multiplied by the number of people and working days, they become a significant share of the available technical time. Management perceives this as structural workload, not as an information access problem.
Then there’s the cost that shows up only once and is the most expensive of all: an expert leaving without their knowledge having been transferred.
How to structure a queryable technical knowledge base
The path has four steps, and the first is the most labor-intensive.
Discovery and acquisition. Identify where the documents are: document management system, network folders, attachments to job orders, paper archives. Digitize what’s still on paper, with quality control on the scans. This phase surfaces duplicates and multiple revisions, and someone has to decide who has the authority to establish which version is valid.
Structuring. Attach to each document the metadata needed to query it: product, family, component, revision, date, language, validity status. This is the work that determines the quality of the answers. Part of it can be automated by extracting from the documents themselves, part requires rules defined together with the technical department.
Linking entities. A manual refers to a product, a product is made up of components, a component appears in multiple bills of materials, a maintenance procedure applies to a family. Representing these relationships makes it possible to answer questions that text search alone doesn’t cover, such as identifying every product that uses a specific component.
Querying with source citation. The answer always states the manual, revision and page. In a technical setting this isn’t optional: whoever is operating needs to be able to verify it, and in case of a dispute needs to be able to show which document it was based on.
After-sales support and multilingual documentation
Two cases deserve attention because the return is particularly direct.
Support. The field technician asks the question in natural language, from the phone, and gets the relevant procedure with the reference to the manual and the correct revision for that serial number. The system can also cross-reference the service history for that product, when it’s available in the management system.
The main effect is on second-level calls: a share of the requests that today reach senior technicians get resolved autonomously. It’s a double benefit, because it frees up the scarcest resource in the technical organization.
Multilingual documentation. A company that exports maintains documentation in multiple languages, with the burden of aligning every revision across all versions. A system that indexes the multilingual corpus makes it possible to query in one language and retrieve content written in another, and to detect which language versions aren’t aligned with the latest revision.
A necessary clarification: automatic generation of technical documentation intended for the end customer requires human validation. Where documentation is relevant to safety or to product compliance, editorial responsibility stays with the technical department.
The indicators
Four measures, to be captured before launch.
Average time to retrieve a piece of technical information, sampled on real requests.
Number of second-level calls, meaning those the first level doesn’t resolve on its own. It’s the most sensitive indicator and the easiest to extract from ticketing systems.
Time to autonomy for a new technician, measured as the interval between onboarding and the ability to handle standard jobs without support.
Share of the technical corpus that’s actually digitized, indexed and has a confirmed revision. It’s a progress indicator, not an outcome one, but it’s useful for governing the rollout.
What this approach doesn’t solve
It doesn’t turn undocumented knowledge into knowledge. If the correct procedure exists only in a technician’s experience, it has to be written down first, and that takes that person’s time. A system can facilitate the collection, not replace it.
It doesn’t solve revision governance. If there’s no rule for who approves and retires a version, the disorder rebuilds itself. In several cases the project’s first effect is exactly this: it makes defining that rule necessary.
It doesn’t remove the need for an expert technician on non-standard cases, which remain the most delicate.
And it doesn’t produce benefits on small, well-organized archives. If the documentation is limited and already structured, the access problem probably doesn’t exist.
In closing
The starting point we recommend is a single product line, with a technical owner involved and a measurement of the starting state. Extending to the rest of the catalog reuses the rules already validated and costs a fraction of the first pass.
If you have a technical archive that people struggle to consult, we’re available for a conversation about that case: what state the documents are in, what questions the system should support, and what indicators would be realistic to measure.
