The knowledge is in the building. It just isn't in the software.

A manufacturing company with forty years of history runs on rules nobody ever wrote down. Which supplier you can push on lead times and which one you absolutely cannot. Why that line is never scheduled at full capacity on a Monday. What "urgent" means when it comes from that particular client. Which three SKUs you never let go below a certain stock level, and the reason has to do with something that happened in 2011.

None of this is in the ERP. So people route around it — Excel, WhatsApp, and the one person who has been there thirty years and holds the whole model in his head.

This is why manufacturing software keeps failing. The features aren't wrong. The operating rules of the business were simply never encoded anywhere a machine could read them. And the people holding those rules are retiring.

There's a broader shift underway here, most visible so far in professional services: the capital of a firm used to be the expertise sitting inside its senior people. Increasingly, the capital is whether that expertise has been turned into a system. Knowledge that stays in heads is a liability with a retirement date.

Knowledge that has been encoded compounds. Manufacturing is the next industry to go through this, and it's the one where the stakes are highest, because the knowledge is more specific, more valuable, and closer to walking out the door.

What we're buildingA general-purpose manufacturing OS: the system a company actually runs on, from orders to inventory to production to the active and passive cycle. Under it, a context layer that builds and maintains a living model of how that specific company works — which is what lets an agent act like that company instead of like a generic assistant. On top, agents that do real operational work.

Getting there means solving something nobody has solved: making a 40-year-old manufacturing business legible to software without flattening what makes it work. Every ERP implementation in history has attempted the opposite — force the company into the software's shape and call the difference "best practice." We do it the other way around, and the reason it's now possible is that the cost of building something specific collapsed.

We're not digitizing factories. We're giving them the infrastructure to evolve with AI — turning industrial SMEs into AI-native businesses, so that every capability that ships over the next decade is something they can absorb rather than something happening to other industries.

We've raised €1.7M from Dig Ventures, Vento, 2100 Ventures and others.



Customers run their entire operations on us. Italy alone has roughly 650,000 manufacturing companies across 141 industrial districts, and almost none of them have software that fits.

What a Forward Deployed Engineer isThe role was invented at Palantir and has since been adopted by OpenAI and Anthropic, among others. The idea is simple and slightly heretical: instead of building software behind a roadmap and hoping it fits reality, you send the person into reality.

An FDE goes on site. Learns how the business actually runs, from the shop floor up. Then builds the ontology: the formal model of that company's objects, processes and rules — what a "job" is here, what states an order moves through, which exceptions are real policy and which are one person's habit. That model is the thing the software runs against. Get it wrong and everything above it is wrong. And you stay until the system is in daily use, which is a very different bar from shipped.

The part that makes it a product role and not a consulting role: everything you learn gets pulled back into the platform. A deployment isn't a project that ends, it's how the product is discovered. FDEs sit upstream of the roadmap, not downstream of it. If what you build stays custom and stays put, the model has failed.

What the role is at ArkeWe have two offices: the customer's plant, and ours.

You'll spend real time on factory floors — in Italy and increasingly abroad. You'll sit with production managers, warehouse staff, owners' sons and daughters, and 60-year-old plant directors who are sceptical of you for the first two days and your strongest ally by the second week.

The work, roughly:

- Map how a company actually operates and turn it into the ontology the system runs on
- Translate what operators tell you into specifications, configuration, and working software
- Build and ship — with AI as your multiplier, which is what makes one person able to do what used to take a team
- Handle data migration, integrations, and all the parts nobody puts in the demo
- Train users and drive adoption, which is measured in whether people still use it in month six
- Feed everything you learn back into the platform
- Join prospect calls, where you'll frequently be the reason a deal closes





Within weeks you'll own a customer end to end. Not assist on one. Own it. Who we're looking forWe're hiring people who intend to be founders.

This role gives you two things a normal job cannot: pattern recognition across dozens of real businesses — their margins, their operations, the reasons they win or struggle — and the experience of owning an outcome in front of someone who is paying for it. That's an unusually good pre-founder education, and we're explicit about it. Some of the people we hire will start companies. We can’t wait to see them shape the world.

We're not looking for a specific degree. We're looking for generalists who make things happen — people who take an ambiguous situation and return with it solved. The strongest profiles we've seen have spent time inside manufacturing: consultants who've run implementations or operations projects, or people who've worked in operations, supply chain or production at a serious manufacturing brand.

If you know what a production plan looks like when it's going wrong, that matters more to us than what you studied.

This is for you if:

- Being on site energizes you rather than drains you. You'd rather be in a plant in Brescia than on a call about the plant in Brescia.
- You find industry genuinely interesting — how things get made, and why a company that's been making them for forty years does it the way it does.
- You build with AI natively or you can’t wait to learn. You don't need to have been an engineer, but you do need to be someone who doesn't wait for permission to try new things and take responsibilities.
- You're comfortable earning credibility in a room where nobody knows you yet, and being the person who says what happens next.
- You want scope before anyone thinks you're ready for it.

This isn't for you if you want a stable desk, a clean backlog, a defined remit, or a remote-first life. Most weeks involve travel. Plans change because a customer's production plan changed.

Practical requirements: Italian and English. A driving license and genuine willingness to travel.

Compensation€40,000 gross, plus a premio tied to successful onboardings — you share directly in the outcome you're responsible for — and meaningful equity.

How to applySend us what you think represents you better: a cover letter, a CV, a link to something you've built. We’ll reach out, set up a first call with our founder, then a call with the FDE team, an onsite aperitivo and offer. The whole process should take around 2 weeks.

[email protected]

📌 Forward Deployed Engineer (Manufacturing) (Milano)
🏢 Arke'
📍 Milano