Avrea Announces its Launch to Build CI/CD for the Age of AI

Avrea, a faster CI for github actions, led by Hannu Valtonen and Juha Valvanne, has announced its launch with $4.7 million in total Pre-Seed funding led by Earlybird.

Before founding Avrea, the team spent years building and scaling technology companies under high-pressure environments, repeatedly encountering software delivery bottlenecks where slow builds, failing tests, and delayed pipelines reduced development speed and productivity.

For years, these inefficiencies were treated as a normal part of software development. But more recently, a fundamental shift began to change that assumption.

Software is increasingly being built alongside AI, dramatically accelerating how quickly developers can generate and iterate on code. However, the infrastructure responsible for testing, validating, and shipping that code has not evolved at the same pace.

Traditional CI/CD systems were not designed for an era where humans and AI continuously produce code changes at scale, turning software delivery, not code creation, into the primary bottleneck.

That shift became the foundation for Avrea, which was created to rethink software delivery from first principles while building a culture centered on speed, trust, ownership, and high-performance collaboration.

Avrea was founded on the belief that the software delivery layer must be rebuilt for the AI era.

The platform is designed to help teams ship faster, more frequently, and with higher quality by enabling continuous collaboration between humans and AI agents.

Integration with existing CI/CD systems requires only a single-line change, while AI agents function as first-class participants in the delivery pipeline, becoming embedded in how software is built, tested, and shipped.

As AI-generated code grows, software quality becomes harder to maintain, with more regressions, flaky tests, dependency issues, and time lost to debugging pipelines instead of building products.

Avrea operates inside CI environments to surface issues traditional tools miss, such as build failures, cache problems, and environment drift, and move from detection to resolution.

The platform automatically identifies problems, proposes fixes via pull requests, and validates changes against full pipelines, with a long-term goal of continuously improving software quality in the background.