$ masli map --as-it-actually-flows
We make companies AI-native — and build it so it still works after we leave.
We start by mapping how the work actually flows — not how the org chart says it does. Then we agree what a good outcome is, in numbers, before anything is built.
- Some of it is advice you can act on yourself
- Some of it we build, adapt, or hand to your team
- All of it ships with the checks that keep proving it works
Engagements
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Consulting & roadmap
from €2,000
We work through where AI actually changes your numbers, and you leave with a written roadmap: what to do, in what order, and what each step is worth. Yours to execute with us or without us.
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Adapted solutions
€5,000–20,000
Proven building blocks fitted to your processes, data and stack, running in production in your own accounts. The handover includes the checks.
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Custom build
Scoped individually
When nothing off the shelf fits. Scope, timeline and price are agreed after the problem is understood, not before.
Method
Four steps, in this order, at every size of engagement.
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01 MAP
Interviews and systems in. Out comes how the work actually moves through your company, including the parts nobody has written down.
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02 DECIDE
We agree what a good outcome is, in numbers, before anything is built. When a result cannot be stated that way, that is worth knowing before the contract.
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03 BUILD
Only what the map justifies. Model-agnostic, in your accounts and your repository, with no hosted service of ours in the middle.
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04 VERIFY
The checks ship with the work. Your team can see the system is still doing what it was built to do, months after we have gone.
Where AI projects stall
Almost none of them fail on the model.
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The pilot works. Production does not.
A demo on ten clean examples says nothing about the inputs that actually arrive on a Tuesday. So the standard is agreed on your real data before the build starts — and if it cannot be met, that is the finding, delivered early enough to be worth something.
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It degrades quietly.
Models get updated, data shifts, prompts drift. The output stays fluent while getting worse, and fluent is exactly what nobody catches in a review. So the checks ship with the system and keep running on their own.
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It runs only while its author is around.
A system one person understands is a system with a resignation date attached. So the reasoning is written down and handed over with the code, in your repository, in a form your team can pick up.