In mission-critical infrastructure software, we’ve long assumed a trade-off: move fast and risk stability, or prioritize reliability and potentially fall behind competitors. It’s a choice every charge point operator knows all too well, because it’s the same choice your platform providers have been making on your behalf.

Six months ago, we decided that trade-off was no longer acceptable.

AMPECO’s CTO, Alexander Alexiev, and I started discussing an idea: what if AI could write all our code by the end of 2025? Not assist developers, not autocomplete lines here and there, but actually write production-ready code. Engineers would architect, validate, and direct the work, but they’d stop writing code line by line. While other companies were exploring how to add AI features into their products, we were asking a different question: could we use AI to transform the way we build software itself?

By the summer of 2025, we had rebuilt our entire development workflow around AI agents. By mid-August, the new system became mandatory for all engineers. The results? A 400% increase in development throughput and a 5x reduction in bugs per feature.

For charge point operators, this isn’t just an internal engineering story. It’s the answer to a question the EV charging industry has struggled with for years.

How do you deliver the features and integrations your customers need at the speed the market demands, while maintaining the uptime and reliability that keeps drivers coming back?

Here’s what most CPOs don’t see: the development engine underneath your platform is a hidden constraint on your competitive speed. Every time you’ve waited months for a critical integration, or held off on a feature or market opportunity because “it’s not on the roadmap,” you’ve felt this constraint.

You just didn’t know it could be solved.

The speed vs. safety paradox

In mission-critical infrastructure, the industry’s bias toward caution was rational. Long release cycles, extensive manual reviews, and conservative change management were the safest way to operate when the EV charging market was young and alternatives were limited.

The risk profile has changed.

The EV charging market is maturing rapidly. Competition is intensifying, customer expectations are rising, compliance requirements are evolving faster, and business models are diversifying.

CPOs still need the same fundamentals: rapid feature deployment to stay competitive, maintain high uptime, integrate with new hardware and energy systems, and adapt as the market evolves.

What’s different is the pressure. Speed can’t come at the expense of stability and stability shouldn’t slow innovation. CPOs need both at the same time.

The iceberg reality

When evaluating platforms, most CPOs focus on what’s visible: features, pricing, and support quality. But what you see is just the tip of the iceberg. Equally important is something less visible: how your provider actually builds and ships software.

The development engine underneath determines everything that matters over time. It determines how quickly new capabilities arrive when you need them. Whether speed comes at the cost of stability, or both improve together. How flexible your platform can be as your business evolves. 

This drove us to ask a fundamental question: what would it take to eliminate the speed-versus-safety trade-off entirely? Not just improve it, but solve it?

We realized the constraint wasn’t our talent—we have exceptional engineers. It wasn’t our commitment—our team has always worked hard. The constraint was the development engine itself.

This strengthened our conviction that the most impactful way to bring AI into the AMPECO platform was to integrate it into our very core. So we did something radical. We rebuilt our entire development process to be AI-native, not AI-assisted, not AI-enhanced, but AI-native from the ground up.

How we rebuilt our development engine from the ground up and became AI-native

Let’s be clear about what we mean by “AI-native,” because the term gets thrown around a lot.

Many products today incorporate AI features, such as chatbots, recommendation engines, or automated reports. These are AI features – visible to users, in marketing materials, and what everyone is racing to ship.

AI-native is something different entirely. It’s AI embedded in how you build software, not what the software does. It’s invisible to your customers, but it transforms everything about what you can deliver to them.

The difference matters. Adding AI features to a product built the traditional way is like putting a faster engine in a car with a manual transmission – you get some improvement, but you’re still constrained by the underlying system. Rebuilding how you build software with AI is like switching to an entirely different propulsion system. The constraints change fundamentally.

When we first proposed this transformation, our engineers were highly skeptical – and for good reason. We’ve always prioritized stable, predictable solutions. And when most developers hear “coding with AI,” they think of “vibe coding”: prompting an AI until the output looks right, then shipping it and hoping for the best.

That approach doesn’t work for mission-critical infrastructure.

Vibe coding is fine for prototypes and proofs-of-concept. But for software that manages charging infrastructure, you need a systematic process where quality is enforced at every step, not hoped-for at the end.

We needed to figure out what that approach was. What does systematic, production-ready AI development actually look like?

So we started with first principles.

Building the CoOperator Dev Agent

The key question wasn’t “can AI write code?” It was “where do humans actually create value in software development?”

We started with a simple principle: humans excel at conceptualization and judgment, AI excels at execution. Understanding what needs to be built, architecting how it should work, validating it works correctly—that’s human judgment. Translating those decisions into code, running tests, deploying changes—that’s execution.

We codified our entire workflow around this principle. The result is CoOperator Dev Agent: a workflow management system where AI handles execution while engineers direct architecture and validate quality.

With over 20,000 automated unit tests, every change is validated before production. This isn’t hoping the AI got it right, it’s systematic quality assurance.

The mindset shift

Building the CoOperator Dev Agent was the easy part. The harder transformation was the mindset shift that needed to occur across our engineering department.

We were asking engineers to stop doing what they’d spent years mastering. Stop writing code line by line. Start architecting systems and directing AI execution instead.

The shift isn’t just about what engineers do; it’s about their level of abstraction. Before, they translated human requirements into code. Now, they translate requirements into instructions for AI, then validate the results. It’s a higher level of thinking, requiring the same deep technical understanding but applied differently.

Engineers have evolved from “bricklayers” writing syntax to “architects” orchestrating logic. They’re no longer writing code line by line. Their level of abstraction is back to human language—but human language that will be read by an AI agent.

Our old bottleneck was too many things to build, not enough engineers. Our new bottleneck? Having verified, well-justified requirements. When code isn’t the constraint, knowing exactly what to build becomes the constraint.

This is why we’re moving all engineers closer to product thinking. Instead of a handful of product managers creating a pipeline for dozens of engineers, we’re building toward a model where engineers explore, research, express intent, and make the judgment calls that shape a great product. CoOperator handles the implementation.

As Alexander puts it simply, “Writing code manually has become obsolete. The expertise now goes into making the automation better, not into repetitive coding tasks.”

The results: speed and quality together

The impact has been dramatic.

Stories that used to take two days now complete in hours. We’ve moved from weekly sprints to daily releases. We’re delivering twice the features with half the bugs.

That last part surprises people. But the data is clear: our bug rate has dropped month over month since we started. Why? Because AI doesn’t get tired, doesn’t take shortcuts, and doesn’t skip tests. And we’re just getting started.

Zero-lag infrastructure: How this impacts CPOs

In software development, there’s always a lag. Lag between identifying a need and scheduling it. Lag between scheduling and starting development. Lag between development and testing. Lag between testing and deployment.

These lags compound. A feature that takes days to build can take months to deliver.

When your EV charging platform provider becomes AI-native, these lags collapse. Not to zero, as there are always some constraints, but to something fundamentally different.

This is zero-lag infrastructure, where your needs translate to platform capabilities without the traditional delays that constrain your business.

Here’s what that means in practice:

1. Faster, more reliable issue resolution

Our development velocity has fundamentally changed. Stories that previously took two days now complete in hours, and with daily releases, improvements reach production continuously rather than waiting for weekly windows

Just as importantly, 50% fewer bugs per story means fewer issues disrupting your operations to begin with. When issues do occur, resolution is 3-4x faster than before.

This speed does not come at the expense of quality as our systematic testing means we can ship fixes continuously without increasing risk.

2. Faster feature delivery and integration velocity

Higher development velocity translates directly into faster time-to-value. Whether you need a new payment processor integrated, a custom reporting feature, or a regulatory compliance update, timelines have compressed dramatically.

With up to 4x faster development cycles, integrations and feature releases that once took quarters can now be delivered in weeks. Actual timelines still depend on complexity and priorities, but the baseline has fundamentally shifted: responding to market and business needs is now measured in weeks, not quarters.

3. Platform reliability that compounds over time

Fewer production incidents disrupting operations, less time on escalations, and more predictable platform behavior. The stability improvement comes from the same system that creates velocity—automated quality gates and comprehensive testing built into every change.

But here’s what makes this different from traditional development: the CoOperator Dev Agent learns and gets more efficient with every cycle, and this advantage compounds. When an issue is identified, we don’t just fix that specific bug—we improve the AI system’s instructions and context, raising the quality baseline for all future development. 

4. Strategic flexibility becomes reality

We built a platform that can adapt to your evolving business model, not the other way around. As the platform development constraint shifts from ‘how fast can we build’ to ‘what should we build,’ we can respond to evolving business needs—whether that’s new pricing models, service offerings, or market segments—without the traditional development bottlenecks. This means our platform doesn’t dictate your strategy—it enables it.

As we’ve unlocked development speed, we’ve pushed constraints forward. Now we’re automating translations, documentation, release processes and streamlining everything that creates lag between “feature complete” and “customer value delivered.”

Why this matters beyond AMPECO

The development engine underneath your platform determines everything that compounds over time: how quickly capabilities arrive, whether you’re forced to choose between speed and stability, and how adaptable your infrastructure is as your business evolves.

The iceberg matters more than the tip.

AI-native engineering isn’t easy. It requires fundamental transformation: re-architecting development processes, massive investments in automated testing, cultural change across engineering teams, and continuous improvement of the AI systems themselves.

But it raises the bar for what CPOs should expect from every platform provider. “AI-native” should become the new baseline. The false choice between speed and safety is over. AI-native development—where zero-lag infrastructure delivers both—is possible today, not in some distant future.