We start with your people, we build on your own data, and we put one digital co-worker into one real process, end to end. When it holds, the same method moves to the next department.
Not because the technology was wrong. Because nobody changed how the work actually gets done.
The team is shown a tool and told to use it. Nobody explains where the project came from, or how the thing actually works underneath. What happens next is simple: they try it once or twice, they do not understand it, they get nothing out of it, and they go back to the old way. Adoption dies quietly, and from the outside it looks like the software failed.
A generic model has never seen your price list, the exception you have granted that customer for twelve years, or the process constraint somebody wrote in a manual in 2019. It answers confidently and it is wrong on exactly the cases that matter. Trust breaks on the first bad answer, and it does not come back.
An idea starts because somebody said AI has to be used: no priority, no owner, no standard to hand it to the second plant. What comes out is a nice exercise with no ROI. A year later you own an experiment, not a way of working.
The company’s memory
It is not a filing problem. That knowledge lives in places that do not talk to each other, and some of those places are people.
It leaves the company with them
Nobody else ever opens it
Only findable by someone who knows it exists
Written once, never read again
It says what happened, not why
It has never been a written question
When one of those people walks out, they do not just take themselves. They take the knowledge, the customers who trusted them, and the ability to put together a good offer. The company does not stop: it slows down, and nobody can say by how much.
This is the first problem we solve, and not with one more place to put files. We read what the company has already written and turn it into a map, and it is the map that makes a digital co-worker trustworthy. It is the step that comes before automation: automating work nobody ever wrote down only means getting it wrong faster.
The first question is never which model. It is which job in the company is worth handing over first. Three things make a process the right one to go first.
It happens the same way many times a week. Once is a project. Many times is a co-worker.
Price lists, past cases, manuals, the system of record. If the knowledge exists only in one person’s head, that person has to write it down first: the AI reads your data and your documents, not people’s minds.
There is a named person who can say an output is right or wrong, and who is measured on it. Without that person there is nothing to approve, and nothing improves.
Finding that process is what the bootcamp is for. We do not guess it from the outside.
The order is not a preference. Each one produces what the next one needs.
Hands-on, at every level. Leadership sees where AI actually pays and where it does not. Middle management learns to redesign a workflow around it, with an owner and a way to measure it. Operating teams work on their own real cases, not demos. You come out with a team that wants it, and a short list of the processes worth doing first. That list is the brief for everything that follows.
Astro is our own platform, and it is what makes a co-worker trustworthy. It reads your documents and builds a living map of how your company actually works, with no ontology to design by hand, and every answer it produces points back to the document it came from. The map stays after we do. That is why the second co-worker costs a fraction of the first, and why the model underneath can be swapped without breaking anything.
We take one process off that list and put it into service. Our team works inside your operation, not beside it: we read the documents and the system extracts, design the job, install it in the workflow your people already use, and sit with the expert who approves the output while it learns. You are not buying hours. You are buying one job, done, in production.
The map
Every document you already own, read once and placedSystem extracts: orders, prices, parts, thirty years of themNo ontology designed by hand. The structure comes out of the documentsEntities: people, plants, products, customers, rulesRelations: what depends on what, and under which conditionEvery answer points back to the document it came fromThis is the company’s memory, in a form a co-worker can use.This is the thing that gets installed, so it is worth being exact about it. Most people assume one of three wrong things.
You do not ask it questions. Work arrives, it does the work, it hands back the output.
Nobody has to remember to use it. It is already in the thread, like a colleague who was copied on the email.
Nothing to install, nothing to log into. It has an address, and it answers.
Four things always true
It does one thing end to end. It drafts the quote, or it plans the shift, or it checks the order. Not “assists with”. Does.
It answers only from your documents and your systems, and every number it produces carries the place it came from.
A named person approves its output before anything leaves the company. Every correction that person makes is in the next draft.
It lives in email, like a new hire. Your people see one colleague, even when several specialised programs are running behind it.
Nine base co-workers
A request for pricing arrives in the shared inbox. It reads the case, pulls the rules, the price list and the comparable jobs from thirty years of archive, and hands back a complete draft quote with the source next to every number. A person signs it before it leaves.
One case, end to end
A request comes in by email, or through the channels you already use, exactly as it does today. Nobody changes their habits.
Astro pulls the rules that apply, the price list, the similar cases from past years, the constraint somebody wrote into a manual four years ago.
A complete draft, with the source sitting next to every number it used.
Your expert reads it, corrects what needs correcting, sends it. Nothing reaches a customer unapproved.
Every correction goes back into the map, so the next draft needs less correcting than the last.
When it does not know, it asks your expert. It does not guess.
The first co-worker pays for itself on the process you install it on: no department is a test bed for the others. What the company inherits is not the bill, it is the method. And the method saves time, not somebody else’s money.
A process is handed over only if it pays for itself on its own cycle. If it does not pay there, it does not happen: the next department is never the justification.
How a process gets chosen, how approval works, what a good output looks like. The data, the people and the decisions stay where they are.
What it cost before, what it costs after, who signed off on the number. Two processes become comparable, even when they run in different departments and plants.
You found out on one process alone, in weeks. Not after rolling it out everywhere, and not after a year.
Which processes are worth doing, how approval works, what a good output looks like: all of it rediscovered at the next company, at the same cost as the first.
Two companies are not comparable when each runs its own experiment with its own definition of success. The fund cannot tell which one is doing better.
At every company the argument is whether AI works at all, instead of which process to hand over first.
Many parallel experiments, each with its own chance of failing, and none of them teaching the others anything.
The first company is where the method gets calibrated, and that playbook moves to the next one. What never moves is the data, the people and the decisions: those stay where they are.
What the process cost before, what it costs after, and who signed off on the number. Two portfolio companies become comparable.
Once one company is running, it is no longer whether this works. It is which process goes first, and when.
And if it does not work, you found out there, on the first one, not on the portfolio.
One real case, from the problem to the number, with the source next to every figure. Where a figure is a projection and not a delivered result, the line says so.
A US iron foundry, around $2B in revenue, multi-plant. A quote request took 10 days to answer, and customers were buying elsewhere while they waited. The quoting team helped build the thing that replaced the wait.
Astro read the quoting manuals, years of past quotes, and 30 years of data from the central system, built the map, and assembled the co-worker. It lives in the quoting inbox. Every draft carries its sources, and a person approves before send.
63% of quote requests answered in under 2 minutes, down from 10 days. In service in under two weeks from kickoff, on roughly two days of our work.
For funds/10
We work with funds that hold manufacturing companies. For one company this is a process that pays for itself. For a portfolio it is something else: a method already calibrated, arriving at the second company without starting over.
The playbook calibrated at the first company moves to the next one. Data, people and decisions stay where they are.
What it cost before, what it costs after, who signed the number. Two companies become comparable.
You start with one process, at one company. If it fails you found out there, in weeks, not across the portfolio a year later.
The honest version, before anybody signs anything.
One process with a name, the documents and system access behind it, and one expert who can say whether an output is right. That is the whole list.
You own the data and the output. We own the platform. The expert who approves is yours and stays yours, and so does everything that gets built on your side.
The bootcamp is days. The first co-worker is weeks, not quarters. If it is going to fail it fails early and cheaply, which is the entire reason for starting with one.
Nothing leaves the company unapproved, so a wrong draft costs a correction and not a customer. Those corrections are the mechanism by which it improves.
Two shapes, chosen before we start. Build fees, a senior team at time and material. Or part of the fee tied to the measured result on the process, when that result can genuinely be measured.
The whole platform, sensitive processing included, can run on your own hardware with a local model. Confidential data never leaves.
Each company’s map, documents, users and permissions are fully separate from every other’s.
Every answer points to the document it came from, so an audit takes minutes.
Public API or local model, chosen per process. The map and the co-worker are the value, and they do not change when the model does.
Astrolize is the Italian operating company of ScaleUp Labs, the applied-AI venture lab based in Boston. From Milan, we work inside the customer context to turn a defined business problem into a working system, then stay through deployment, measurement and iteration.
Meet Astrolize
Pick the process that repeats every week and is costing real time. We will look at it with you and tell you whether it is a good first job, including when the answer is no.