A big thank you to our entire team, our partner ecosystem, and the customers who trust us. This is just the beginning.

At Valven, we set out to build a developer productivity insights platform to improve software delivery performance combining AI-driven insights, predictive analytics, and workflow automation across the entire SDLC. Legacy approaches were breaking under the same pressures:

• Fragmented SDLC data across tools

• Increasing complexity with AI coding tools

• And a growing gap between activity and actual delivery outcomes

A Different Approach to Developer Productivity

Most engineering analytics platforms focus on collecting and visualizing data. They pull information from different systems, aggregate it, and present it through dashboards. On the surface, this creates visibility. In practice, it often creates distance. Teams can see what is happening, but they still struggle to understand why it is happening or what to do next.

The problem is not a lack of data. It is the way the system is modeled.

Software delivery does not move in clean, isolated steps. It does not stop with coding, then review, then testing, then release. Work flows back and forth. It gets blocked, reworked, delayed, and reshaped along the way. When you analyze each part separately, you lose the connections where most problems actually form.

Valven approaches this differently by treating the entire software delivery lifecycle as a connected system. Instead of looking at individual metrics in isolation, it brings together code changes, pull request activity, review behavior, rework patterns, and delivery timelines into a single model. This makes it possible to understand how issues develop across the system, not just where they appear.

As Gartner writes in the report:

“Valven provides an AI-powered engineering intelligence and software delivery management platform that integrates with development pipelines to provide deep visibility into the SDLC. By automatically tracking delivery metrics, analyzing work patterns and using AI to forecast sprint outcomes, it empowers engineering leaders and product managers to eliminate bottlenecks, optimize resource allocation and continuously accelerate software delivery.”

- Gartner, Magic Quadrant for Developer Productivity Insight Platforms, Frank O'Connor, Peter Hyde, Akis Sklavounakis, Akriti Kapoor, May 5, 2026.

Built for the Reality of AI-Driven Development

This difference becomes even more critical in AI-driven development.

AI has significantly increased the speed of code production, but it has also introduced new types of risk. More code does not necessarily mean more progress. In many teams, it means review processes are under pressure, low-quality changes scale faster, and rework becomes harder to detect early. Traditional metrics still interpret this as productivity because output is higher. In reality, the system may be degrading.

Valven does not treat output as a reliable indicator on its own. Instead, it focuses on how work behaves inside the system. It looks at where reviews slow down, where rework accumulates, and how risk builds up across changes. This allows teams to see issues that would otherwise remain hidden behind healthy-looking averages.

From Visibility to Action

In most tools, visibility is where the story ends. A dashboard highlights a trend, and the responsibility to interpret and act on it is left to the team. This is one of the main reasons analytics often fail to create real impact. Seeing a problem is not the same as knowing how to respond to it.

Valven is designed to close that gap. Instead of only showing what changed, it helps explain where the issue is concentrated, what is likely causing it, and where intervention will have the most impact. This shifts the role of analytics from passive observation to active decision support.

At the same time, these capabilities are built with enterprise environments in mind. Different organizations have different constraints around data, security, and deployment. Some require fully controlled environments where data never leaves their infrastructure. Others need to scale across multiple teams and regions without losing context. Valven supports these requirements without changing how the system is modeled, which allows it to remain consistent as organizations grow.

Ultimately, this recognition is not about visibility on its own. It reflects a change in expectations.

Engineering organizations are no longer looking for more dashboards. They are looking for systems that help them understand complexity, anticipate problems, and act earlier. The shift is moving from reporting what already happened to understanding what is about to happen.

As software development continues to evolve with AI, this need will only become more pronounced. The teams that adapt will not be the ones tracking more data, but the ones that can turn that data into clear, timely decisions.

This is the direction Valven represents, and this recognition is a reflection of that shift.