We analyse your data and your structures in detail. Together we identify the two or three cases that tie up the most engineering capacity. And we implement the first one so that it becomes the foundation of your AI structure. So your engineering develops again instead of administrating.
In many engineering organisations every technical clarification runs through the same few people. A design engineer spends hours looking for old calculations, sales waits for an answer, the experienced colleague becomes an internal helpdesk. The knowledge is there, in project folders, emails and heads, it is just not retrievable. What is missing is not skill, but free capacity.
A knowledge layer makes existing know-how directly retrievable, always with a source. The routine runs automatically, the tricky cases stay with your experts. The freed capacity is the raw material for what makes the difference: new products, faster development, stronger differentiation. Engineering develops again instead of administrating.
Are the existing processes and data even suitable? Which area first? What does the first step actually deliver? This is where it fails in most companies, not on the technology.
Requirements, prototype, acceptance, series production. Success criteria in writing, tested with real questions, go or no-go. No slides, no gamble. The first step is an analysis for EUR 1,200.
Answers queries from design, sales and service, with sources. Runs permanently and is kept current as new data comes in. Example: sizing knowledge on demand.
The roles are clear: we design everything. Concept, workflow, rules and target structure are developed together with your people. The AI agents only handle the execution: bills of material, master data and legacy projects that used to tie up staff for months, following the defined rules, with sources. Your engineer reviews and approves. Months become days.
This is exactly the foundation that standardisation and engineering excellence rest on. Only once bills of material, master data and variants are clean do the structural topics hold: product structure, variants, standardisation.
Your entry point is a structure and data analysis for EUR 1,200 net. Then the priority workshop, then the proof of concept at a fixed price. By the time you decide on the POC you have invested EUR 4,000, not EUR 40,000. You can stop after every stage. What has been built is yours.
In a conversation you describe what eats up capacity. You share one or two data packages, we analyse them in detail.
EUR 1,200 net · a few daysShow details +Building on the assessment we develop the concept with your team: how to reduce the effort and create a workflow that becomes a competitive advantage.
EUR 2,800 net · 1 dayShow details +The most important case on your real data, tested with real questions. Your team assesses and refines it. The result holds even on a no-go.
EUR 9,800 fixed price · 1 weekShow details +If we decide together to implement, we build the foundation of your AI structure: hosting, model choice, permissions, internal champion.
4–8 weeks · per the cost corridor from the POCShow details +Keep it current, refine it, build on the foundation. The structural topics follow once capacity has been freed.
Monthly retainerShow details +These are the cases that tied up the most capacity at other companies, and the agents that came out of them. The same agent, five applications. Each works on your data and answers with sources.
Win design time back. No more hours spent searching for old projects, variants and calculations. Senior staff out of the helpdesk role.
Cut downtime. Fault patterns, spare parts and service history at hand, even when the senior technician is not available.
Variant knowledge available. The right variant with standard and evidence, consistent product information for sales and distribution.
A faster inside sales team. Data sheets, spare parts and sizing at hand. More quotations and more revenue per head with the same team.
The work nobody wants to do. The agent prepares the groundwork for risk assessment and documentation, the engineer reviews and approves. Reviewing is faster than writing.
This is what the result of a structure and data analysis looks like in practice. Plant engineering use case: ten design engineers, around 100 projects, six years of history on network drives, in emails and in people's heads. That is how much capacity sits in searching alone.
If the knowledge agent intercepts 70 to 80 % of these queries, more than 300 hours of engineering time per year are freed in this example. That is a typical target, not a guaranteed result. This is exactly why the path starts with analysis and priority workshop: that is where we define in writing what the POC is measured against. After one week you know what the implementation delivers, what it costs, or why it is not worth it.
ERP, PLM, CAD, documentation, M365. No parallel system.
Azure, M365, SharePoint, Teams, your identities.
Processing in the EU. EU cloud to fully on-premise.
Every action logged. Source, prompt, result.
Tell us in 30 minutes where things get stuck. If it fits, the structure and data analysis for EUR 1,200 follows. If either side sees no potential, we part ways at that point.
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