AI when the problem calls for it, automation when automation is enough. The point is never the technology. It’s the result.
Not everything is worth the same. Some of what you run should be replaced with something robust, some of it never existed and has to be built, and some of it is not worth touching. That sorting happens on your site, inside your processes, with your teams. And we tell you where AI actually wins, and where a plain automation is enough.
With the right tools and know-how, quality, production-grade software gets built in days. The time goes somewhere else.
Implementation isn’t the hard part any more. Knowing exactly what to build is.
One to three days, on site. We watch what people really do, not the org chart. You leave with a written plan: what, in what order, what it costs, what it returns, and what isn’t worth touching.
Days or weeks, not months. At the end it runs for real, not as a demo. Your teams are trained on it, everything is documented, everything is visible.
If something breaks, we are the ones who tell you. Not your customer. And you can stop whenever you want: nothing collapses when we leave.
A business where the sale happens on the phone. The calls had been recorded for years and nobody listened to them: an attentive manager hears five in a good month. That was not wasted time. It was a capability they did not have.
It all runs on their accounts, in their database, under their name. Every analysis can be replayed, every version of every instruction is dated, and when a stage is skipped the system says so instead of pretending.
Here is the question nobody asks in the first meeting: what happens the day we stop working together? This is our answer, in writing, before anything starts.
This is not generosity. It is the condition for owning the thing rather than renting it.
Forward deployed engineering: automation and applied AI, built inside your systems and left running. Based in Toulouse, working on site and remote.
For ten years we ran engineering teams. Which means: we were the ones woken at three in the morning when the system went down. You learn quickly, in that job, to tell the difference between what works in a demo and what is still standing two years later.
The last five we spent building AI systems in production: agents, call analysis, AI grounded in company documents. Enough to know when AI settles a problem and when it adds one.
Today we don’t run teams. We build, we ship, and we stay while it runs.
Alongside client work, we build our own tools for agents: their memory, a map of what a company runs, contracts between agents that don’t know each other. Three are already public, and we run all of them ourselves before we put them in front of you. See our tools at cloud.montytorr.com →
Tell us in two lines what is eating your time. We reply within one business day.
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