AI for manufacturing
Technical knowledge the moment a line goes down — with the source shown.
Machine manuals, service procedures, standards — locked in PDFs and in people's heads. During downtime every minute counts, and the technician loses it hunting for the right page in a catalogue. We build a knowledge base that answers from your documents and cites the passage — on AWS, with no data leaving your account.
The problem
The technical knowledge exists. Access to it does not.
Every plant has tonnes of documentation: machine manuals, service instructions, maintenance procedures, quality standards. The problem is not a lack of knowledge — it is how scattered it is. A PDF from 2014, a folder on a server, a note in an experienced worker's head. During a breakdown the technician searches instead of acting.
A generic AI model will not fix that. ChatGPT does not know your machines, your plant or your documentation — and it will guess from general knowledge. RAG reverses the order: it first retrieves the right passage from your knowledge base, then answers — and shows where the answer comes from.
Use cases
Where it genuinely saves time and protects knowledge.
Not machine control. Not failure prediction. Access to the right information — faster and with a source citation.
Technical knowledge base
Manuals, service instructions, procedures, standards — one place, one query. A technician types "replacing X47 seals" and gets the right procedure with a chapter number, not a list of results to wade through.
Maintenance support
During a breakdown every minute costs. Instead of phoning around the plant and hunting for the right PDF — the technician asks a question and gets an answer with the source. The decision still belongs to the human — the system supplies the right information, it does not make the call.
Knowledge transfer and onboarding
An experienced worker leaves — their knowledge does not. If the procedures are in the base, a new technician can ask about what they do not yet know and get a concrete answer from the documentation.
Search across quality documentation
Standards, control plans, non-conformance reports — quality documentation is another source the AI can answer from. An internal audit asks, the system points to the right passage.
Security and the AI Act
Your data stays with you. We verify the AI Act risk classification during the audit.
Data and know-how stay in your AWS account
The knowledge base is built in Amazon Bedrock Knowledge Bases, in your cloud account — documents, procedures and know-how do not travel to public models. For intellectual property and trade secrets that is a precondition.
The AI Act: we verify the classification, we do not scaremonger
Technical knowledge bases are usually not high-risk systems under the AI Act — but it depends on the use case, and some obligations (such as transparency toward users) can apply to lower-risk systems too. Verifying the classification is exactly where the audit begins.
Citations and human oversight
Every answer carries a source — the technician sees where the information comes from and can verify it. The system supports the decision, it does not replace it. That is both an engineering and a legal requirement.
Proof
The same engineering stack — built end-to-end on our own product.
100%
AI extraction accuracy on packaging data (validation set, n=200)
$0.0006
cost per AI scan in production
302,516
drug-interaction records in the production knowledge base
The numbers come from mojApteczka — a product in the healthcare domain that we built and run in production (RAG with citations, evaluations, an AWS backend). It is the same architecture we will bring to a manufacturing plant. We are not publishing client case studies from the manufacturing sector yet — coming soon.
See the full case studyQuestions
Before you ask
No. We build the knowledge base in your own AWS account (Amazon Bedrock Knowledge Bases). The documents stay under your control — which matters for trade secrets as well as GDPR.
Have technical documentation your AI should answer from?
Let's start with an audit: we'll check the state of your sources, the scope and what can be shipped quickly — without experimenting on your budget and without promising numbers we cannot guarantee.