AI Factory & AI automation
Systematically automate recurring knowledge work. With processes, tools and roles that work in everyday practice.
AI Factory
Everyone talks about AI. Few have scaled it.
70 percent of B2B companies plan their largest AI investments in 2026. Only 10 percent have scaled them, and only 8 percent have an AI strategy with measurable goals and clear ownership. The reason is rarely the technology. What is missing is the system: pilot projects run on the side, results stay within the team, knowledge remains scattered.
The AI Factory is our answer to that. A system of processes, tools and roles that makes recurring tasks reproducible. Born out of our own project business, when we were 30 percent too expensive, and tested in client projects ever since. We start where measurable time is lost in day-to-day work.
LET'S TALK!
Find your most expensive loop now
Bring your blind spot along, we'll build the loop around it.
Your benefit with your own AI Factory
The entire value chain
Development accounts for only 35 percent of the effort. We also optimise requirements engineering, project management and QA. Only the sum creates an effect that counts.
Loop instead of full automation
AI writes, people review. We're not abolishing refinement, but the manual work. That way quality stays verifiable and responsibility stays clear.
Knowledge the AI finds
Transcripts, requirements, decisions and metrics in one place, structured and accessible. AI is only as good as what it can find.
Roles that carry it
Makers build tools and automations, amplifiers bring them into everyday use. That way the change endures even after the project ends.
Measurable instead of buzzword
We measure which process takes longest and tackle the biggest pain point first. Results in hours and euros, not slides.
References
AI Factory: results from practice
From in-house project business to the ordering process in B2B wholesale. Examples showing what a system of processes and roles can achieve.
Project Schmersal: from too-expensive offer to contract award
The client wanted the project with us; our quote was well above budget. Instead of just optimising development, we involved all disciplines: requirements, project management, QA and development.
Ehlert: order acceptance in B2B wholesale
97 per cent of orders didn't come through the shop, 57 per cent of those by e-mail. Order intake now runs automated.
Internal: knowledge vault and forecast
Transcripts, requirements, decisions and controlling metrics in one place, complemented by an app for utilisation and revenue planning. This knowledge is the basis for every further automation.
The AI Factory isn't a consulting product, it was our own way out. What we recommend to clients, we first measured against our own project business.
Your path to your own AI Factory
From first measurement to anchored system. Four steps that turn individual AI experiments into a reproducible process.
Vitality check & process analysis
We look at your workflows from the outside: which process takes longest, where are the time thieves, how is your knowledge structured and accessible? Not guessing — measuring.
Hackathon instead of workshop
Five days, all trades, real tasks from your business. Instead of theory or a miniature PoC, you get robust results and an honest assessment of what holds up and what doesn't.
Build & integrate loops
From these insights come tools: skills, slash commands, agents and MCP connections to Jira, Confluence, Git and ERP. Every automation with a defined review loop.
Anchor & scale
Makers and amplifiers take over, learnings flow back into the system, building blocks move on to the next project. Every cycle makes the next one faster.
What the AI Factory moves, measurably
28 %
cheaper offer
In the Schmersal project, after all trades were optimised
5 min
saved per order
Through automated order intake at Ehlert
2 weeks
through to automation
From analysis to ongoing order processing
KNOWLEDGE TO TAKE AWAY
Guide & Insights
Frequent questions on AI Factory
A car factory produces replicable parts: always the same, always fast, scalable. An AI Factory does the same with knowledge work. A system of processes, tools and roles that makes recurring tasks reproducible, rather than merely speeding them up once.
No. IT projects are our proof, the principle works wherever knowledge work is repeated: order entry and routing in logistics, claims processing in insurance, quotes and research in consulting.
Not with a big bang. We first look at which process takes longest and who owns it, review the knowledge structure and start with one area. A quick win that is noticeable carries further than a big plan.
Instead of asking once whether a company is AI-ready, we continuously observe how AI is used in actual tasks. The check uncovers gaps, duplicated effort and commonalities between projects and turns these into concrete measures.
Paid work without value creation: searching instead of finding, copy-paste between tools, meetings with no outcome. We ask the people who actually do the work and give them low-threshold tools that save time day to day.
Yes, otherwise it stays at assistance level. Confluence, Jira, Git, ERP: what matters is who has sovereignty over the data. We clarify governance, information architecture and access rights before automating. No reliable results without clear rules.
Every three to six months state of art shift. So we don't build bespoke solution on one model, but swappable building blocks and loop feeding learnings back. AI adoption isn't a project, it's an ongoing process.
Before the start we measure effort per process step, then measure the same metrics again afterwards: processing time, cycle time, share of manual steps. This makes the effect provable, not just felt.
LET'S TALK!
Von der Idee zum ersten produktiven Use Case.
In the discovery call, we clarify the starting position, data situation and target vision, and what a realistic first step towards your AI factory looks like.
Please accept the corresponding cookies to view this embedded content.
