Why DevPrism exists
One leadership meeting, one question, and no answer. That is where this company started.
The question that started DevPrism
Are developers actually more productive with AI?
A leadership meeting, somewhere inside an engineering organisation. The CTO asks the million-dollar question. Across the table, a VP of Engineering running several dozen engineers and technical experts — with no answer, and no way of getting one.
VP Engineering — We can look at the number of tickets shipped over the last few sprints.
VP Engineering — Except that proves very little. A ticket can take an hour, or five days.
CTO — But do we at least know who really uses it? And where it works?
VP Engineering — We know who uses it. And that is all.
No way to assess real impact, no reconciliation or correlation between the tools already in place, not a single ROI indicator. At a time when tokenmaxing is spreading, the question stops being a technical detail and becomes a boardroom matter.
The market was reviewed in depth in early 2026: vendors met, demos attended, hard questions asked. Nothing was signed — what was being looked for did not exist in that form yet. The market has evolved since, but the original question has lost none of its importance. DevPrism was built to answer it.
The question one floor up
There is a harder version of the same question. An executive hears that peers ship twice as fast since Cursor or Claude, looks at their own engineering organisation, and sees nothing change. They do not want another dashboard. They want to know whether they are being told stories, or whether the bottleneck is genuinely at home.
And the answer is rarely that teams are not using AI. They use it, and they really do save time. But that time never surfaces anywhere: it is absorbed by review backlogs, by round trips between teams, by work that has to be redone, by approvals that drag on. The bottleneck has moved — and nobody has located it yet.
Measuring adoption answers none of this. Meeting that challenge head-on is exactly what DevPrism was designed for.
What we build because of that
Correlation, not adoption counts
AI usage placed next to delivery speed, code health and cost — so a number can carry a decision instead of illustrating one.
Investigation, not visualisation
Agents that go and look for why a metric moved, and come back with an explanation you can act on.
Graduated autonomy
From suggestion to automation, level by level, controlled by your own rules. You set the pace.
European compliance
Hosted in France, GDPR and the AI Act built in from the design stage, and source code that never leaves your repositories.
Our ambition
The engineering intelligence market was built around measurement. We start from the opposite premise: measuring is only the prerequisite. What makes the difference is the decision that follows, and the action that carries it out.
That is what DevPrism is for — a control plane for AI-assisted engineering. Agents that investigate, then propose, and finally act, under a policy you write and at a level of autonomy you raise when you are ready. Levels 0 to 4 are shipped. Level 5 is coming soon.
A European SaaS platform, by design. Governing AI agents is still largely open ground, in Europe as elsewhere, and that is precisely where we chose to stand: a platform hosted in France, designed for GDPR and the AI Act from the start rather than brought into compliance after the fact.
DevPrism is a young company, but its ambition is to turn engineering measurement into a decision system and an optimisation engine — far more than one more report.
Same question at your place?
If “are we shipping faster with AI?” has no answer in your own leadership meetings, that is exactly what we should talk about.