Too specific for standard software. Pragmatic enough to start focused.
Important work that standard tools fail to understand, but teams know how to describe.
The work has evolved. The solutions have not kept up.
Your tools no longer match the real work
Teams move between systems, re-enter information or maintain their own files just to complete the work.
Exceptions matter as much as the standard case
The workflow depends on business rules, documents, human decisions and expertise that cannot be reduced to a form.
The problem is operational before it is technical
You see delays, rework, constrained capacity or missed opportunities without yet knowing which solution to build.
You want proof before expansion
You would rather solve one specific workflow and measure the result than immediately fund a broad transformation program.
Between generic software and a large consulting program.
We bring a small team accountable from assessment through deployment. The scope stays focused, users stay involved and every investment answers an observable decision.
A strong project starts with the right conditions.
The Otto method reduces uncertainty, but it does not replace access to real work or company decisions.
One specific workflow
We need to observe a flow, its inputs, outputs and main exceptions.
Measurable stakes
Time, delay, quality, capacity, margin or revenue provide a baseline for the project.
Available people
Users, the sponsor and IT participate whenever their knowledge or decision matters.
Accessible information
The necessary tools, documents and data must be reviewable within an agreed framework.
Saying no is also part of the method.
You only want a generic chatbot
Our work begins with a workflow and an expected result, not with technology to install everywhere.
No one can describe or test the workflow
Without access to the field, we cannot build around the real rules and exceptions.
The issue has no volume or visible consequence
Custom development must address stakes large enough to justify building and operating it.
We have also documented two environments where these conditions often appear: industrial distribution and maintenance.
Let’s find the right place to start.
A 30-minute conversation to understand how you work, what is getting in the way and where AI could genuinely help.