Field service and maintenance support
Technicians querying service manuals, wiring diagrams and prior repair histories from a device on the factory floor, working offline where connectivity is unreliable.
AI & RAG Consulting for Manufacturing and Energy
Retrieval over technical documentation, maintenance records and engineering drawings, including the scanned, decades-old material most systems cannot read.
Frameworks I design against
- EU AI Act
- NIS2
- Machinery Regulation
- ISO 27001
- Sector safety standards
Short answer
Rubra Digital builds retrieval systems for manufacturers and energy companies over technical documentation, maintenance histories, engineering drawings and inspection reports, including the scanned and legacy material where document parsing rather than model choice decides whether the project works.
Last reviewed
Where I see this working
Engineering documentation search
Retrieval across decades of specifications, change notices and as-built drawings, where the authoritative version is frequently the hardest thing to establish.
Root cause investigation
Surfacing comparable historical failures and their resolutions from maintenance and quality records during an active investigation.
Supplier and specification compliance
Checking incoming supplier documentation against your specifications and flagging deviations for engineering review.
Health and safety procedure access
Fast, reliable retrieval of the applicable procedure, with the revision and effective date attached to every answer.
Industrial retrieval projects are decided in the document parsing stage. Model choice barely matters if the maintenance manual is a 1997 scan with the torque specification in a table that OCR renders as a column of unrelated numbers.
What makes this sector distinct
The documents are hostile. Scans of varying quality, engineering drawings where meaning lives in callouts and title blocks, tables that carry the actual answer, and mixed languages within one document set. Layout-aware extraction is mandatory, not an optimisation.
Versioning is the real question. “What is the torque specification?” has a different answer depending on the revision, the serial range and whether a service bulletin superseded it. A system that returns the right value from the wrong revision is worse than one that returns nothing, so version and effective date are retrieval metadata, not presentation detail.
The stakes are physical. A wrong answer about a procedure can injure someone. These systems are designed to cite precisely and refuse clearly, and to be positioned as a way to find the authoritative document rather than as a substitute for reading it.
Connectivity is not guaranteed. Plant floors, offshore platforms and remote sites need graceful degradation rather than an error page.
How I scope it
I ask for a sample of your worst documents in week one, not the clean ones. The recoverable proportion of that sample sets the scope of the whole project, and it is far better to know in week one than in month three.
Where OCR quality is not good enough, I say so and scope the remediation separately rather than quietly building a system that will return confident answers from garbled source text.
Frequently asked questions
Can these systems work with scanned drawings and old PDFs?
Yes, but this is where the engineering effort actually goes, and it is the part most proposals underestimate. Scanned documents need OCR quality good enough that retrieval works, which for engineering drawings means layout-aware extraction rather than plain text OCR, since the meaning sits in tables, callouts and annotations. I assess a sample of your worst documents in week one and tell you honestly what is recoverable, because that assessment determines the project more than any model decision.
What about equipment documentation from suppliers who no longer exist?
This is extremely common in plants with thirty-year-old assets and it is one of the strongest arguments for doing this work at all. The documentation exists as scans in a file share, nobody can find anything in it, and the people who knew it are retiring. Making that corpus retrievable captures institutional knowledge that is otherwise actively disappearing.
Can it run without reliable internet connectivity?
Partially. Retrieval over a local index runs comfortably on modest on-premise hardware. Generation is the harder half. A small local model handles straightforward lookups acceptably, but quality drops noticeably on complex synthesis. A common pattern is a local system that degrades gracefully: full capability when connected, retrieval with extractive answers when not.
Get a straight answer on your AI roadmap
A 30-minute call with the engineer who would do the work, not a salesperson. You will get an honest read on what is worth building, what is not, and roughly what it costs.
No NDA needed to talk. EU and UK hours in full, with afternoons overlapping US Eastern and Central.