For a long time, AI in software development lived on the margins — demos, side projects, internal experiments.
What’s changing now isn’t capability, but placement. AI is starting to show up inside day-to-day engineering workflows, particularly in environments with large, long-lived codebases.
On Kavia alone, more than 1,000 active projects are running today across enterprise teams. These aren’t greenfield experiments — they include legacy modernization, embedded systems, and safety-critical software.
In those environments, productivity isn’t theoretical. Teams report measurable improvements, not because AI replaces engineering judgment, but because it reduces the cost of understanding complex systems.
That’s what it looks like when AI stops being experimental and starts becoming infrastructure.
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