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December 1, 2025

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April 10, 2026

Franco Possamay, Full-Stack Developer at Kaizen Softworks

Franco Possamay

Peñarol football fan

Full-Stack Developer

How We Use Code Reviews to Build a Culture of Quality in a Growing Team

Published on

·

April 10, 2026

Last updated on

·

April 10, 2026

Time to read

·

12

Franco Possamay, Full-Stack Developer at Kaizen Softworks

Franco Possamay

Full-Stack Developer

I’ve been on this project for years. I was here when "the team" was just two developers in a room, and I'm here now as part of a 30+ person force building a critical logistics platform for our client.

Most stories about scaling a dev team are war stories. They’re all about shipping fast, racking up technical debt, and eventually having to rewrite the whole thing from scratch. Ours is different. It’s about how a deliberate (some might say stubborn) commitment to code quality from day one helped us grow without burning ourselves later.

A Pact for Quality in a Sea of Legacy Code

When Kaizen joined this project, we weren't starting with a blank slate. We were tasked with merging and modernizing several legacy systems into one cohesive platform. The codebase had layers, each with its own quirks and logic. In that kind of environment, the “move fast and fix later” approach would’ve been a disaster.

We knew that if we wanted to build something scalable and sane to work with long-term, quality couldn’t be optional. It had to be baked in.

So from day one, we made it a rule: every single line of code gets reviewed. Whether it’s a new feature or a legacy refactor, it goes through a second pair of eyes.

We framed this to our client not as a slowdown, but as an investment. Spending an extra hour reviewing a PR, especially one touching old code, saves days (sometimes weeks) of tracking down weird bugs later. It was the only way to build a modern, maintainable codebase.

Fortunately, our client’s technical. They got it. And that trust? It became one of the most valuable things in our partnership. It gave us room to do things right, not just fast.

Making Code Reviews a Team’s Habit

We needed a process, a way to keep standards high, share knowledge, and catch issues early. So we built a structured code review system, grounded in Kaizen’s core principle: continuous improvement.

This wasn’t about nitpicking or finger-pointing. It was about creating a feedback loop and turning every review into a learning opportunity.

Here’s what that looks like today:

  • Dedicated Review Stage: When a dev finishes a story, it moves to “Code Review” in Jira. It’s not a nice-to-have, it’s a required step.
  • Peer-Powered: Another dev from the same sub-project picks it up. They already have context and can give meaningful feedback. We use Azure DevOps to review, leave comments, and suggest changes.
  • Beyond Just Bugs: We ask more than “does it run?” We ask:
    • Does it actually solve the problem the ticket described?
    • Is there a better, more efficient way to do this? (e.g., “Could these five DB calls be one?”
    • Does it follow our naming conventions and practices?
    • Is the code readable and maintainable for whoever works on it next?

Scaling the Blueprint

Going from 2 to 30 devs is one thing. Scaling a culture of quality across all of them? Way harder.

Our code review process became the backbone of both onboarding and team growth.

  • The Wiki = Our Shared Brain: Standards don’t work if they only live in someone’s head. We documented everything in an internal Wiki, from naming conventions to architecture decisions. Reviewers link to it often, turning corrections into coaching moments.
  • Reviews as Mentorship: For new devs, their first code reviews are where the real onboarding happens. They get real feedback from teammates who know that part of the system inside and out. It’s how we share the team’s DNA. It's how we say, “This is how we do things here.”
  • Same Rules for Everyone: Junior or senior, every dev’s code goes through the same review process. It keeps egos out of it and keeps the bar high across the board.

The Payoff: Growth Without the Chaos

Was this a quick win? No. It took time and buy-in. But the impact has been huge.

“I remember a case where we brought a function’s execution time down from 10 seconds to under three. The client was thrilled,” one of our devs shared. “That’s when it clicks. This isn’t just about ‘clean code’, it’s about delivering real value.”

Some of the benefits we’ve seen:

  • Reduced Rework: Catching logic errors in reviews cut down on production issues.
  • More Trust: Our client saw the difference. They knew we weren’t just ticking boxes, we were building for the long haul. That made it easier for them to vouch for us internally and bring us more projects. 
  • Better Knowledge Sharing: Juniors learned from seniors. Seniors got fresh eyes on their code. Everyone leveled up.
  • Smooth Onboarding: With strong processes and a healthy codebase, bringing in new devs got easier. We could grow without sacrificing quality.

And yeah, we still have technical debt. We even have a “tech debt bucket” in Jira. But it’s not this ominous, growing monster anymore. It’s a manageable list. A conscious decision, not a side effect of bad habits.

Wrapping Up

Code reviews aren’t magic. They won’t solve everything. But done right, they can become the heartbeat of a team that cares about quality, about learning, and about building something that lasts.

That’s how we did it. That’s how we’re still doing it.

I’ve been on this project for years. I was here when "the team" was just two developers in a room, and I'm here now as part of a 30+ person force building a critical logistics platform for our client.

Most stories about scaling a dev team are war stories. They’re all about shipping fast, racking up technical debt, and eventually having to rewrite the whole thing from scratch. Ours is different. It’s about how a deliberate (some might say stubborn) commitment to code quality from day one helped us grow without burning ourselves later.

A Pact for Quality in a Sea of Legacy Code

When Kaizen joined this project, we weren't starting with a blank slate. We were tasked with merging and modernizing several legacy systems into one cohesive platform. The codebase had layers, each with its own quirks and logic. In that kind of environment, the “move fast and fix later” approach would’ve been a disaster.

We knew that if we wanted to build something scalable and sane to work with long-term, quality couldn’t be optional. It had to be baked in.

So from day one, we made it a rule: every single line of code gets reviewed. Whether it’s a new feature or a legacy refactor, it goes through a second pair of eyes.

We framed this to our client not as a slowdown, but as an investment. Spending an extra hour reviewing a PR, especially one touching old code, saves days (sometimes weeks) of tracking down weird bugs later. It was the only way to build a modern, maintainable codebase.

Fortunately, our client’s technical. They got it. And that trust? It became one of the most valuable things in our partnership. It gave us room to do things right, not just fast.

Making Code Reviews a Team’s Habit

We needed a process, a way to keep standards high, share knowledge, and catch issues early. So we built a structured code review system, grounded in Kaizen’s core principle: continuous improvement.

This wasn’t about nitpicking or finger-pointing. It was about creating a feedback loop and turning every review into a learning opportunity.

Here’s what that looks like today:

  • Dedicated Review Stage: When a dev finishes a story, it moves to “Code Review” in Jira. It’s not a nice-to-have, it’s a required step.
  • Peer-Powered: Another dev from the same sub-project picks it up. They already have context and can give meaningful feedback. We use Azure DevOps to review, leave comments, and suggest changes.
  • Beyond Just Bugs: We ask more than “does it run?” We ask:
    • Does it actually solve the problem the ticket described?
    • Is there a better, more efficient way to do this? (e.g., “Could these five DB calls be one?”
    • Does it follow our naming conventions and practices?
    • Is the code readable and maintainable for whoever works on it next?

Scaling the Blueprint

Going from 2 to 30 devs is one thing. Scaling a culture of quality across all of them? Way harder.

Our code review process became the backbone of both onboarding and team growth.

  • The Wiki = Our Shared Brain: Standards don’t work if they only live in someone’s head. We documented everything in an internal Wiki, from naming conventions to architecture decisions. Reviewers link to it often, turning corrections into coaching moments.
  • Reviews as Mentorship: For new devs, their first code reviews are where the real onboarding happens. They get real feedback from teammates who know that part of the system inside and out. It’s how we share the team’s DNA. It's how we say, “This is how we do things here.”
  • Same Rules for Everyone: Junior or senior, every dev’s code goes through the same review process. It keeps egos out of it and keeps the bar high across the board.

The Payoff: Growth Without the Chaos

Was this a quick win? No. It took time and buy-in. But the impact has been huge.

“I remember a case where we brought a function’s execution time down from 10 seconds to under three. The client was thrilled,” one of our devs shared. “That’s when it clicks. This isn’t just about ‘clean code’, it’s about delivering real value.”

Some of the benefits we’ve seen:

  • Reduced Rework: Catching logic errors in reviews cut down on production issues.
  • More Trust: Our client saw the difference. They knew we weren’t just ticking boxes, we were building for the long haul. That made it easier for them to vouch for us internally and bring us more projects. 
  • Better Knowledge Sharing: Juniors learned from seniors. Seniors got fresh eyes on their code. Everyone leveled up.
  • Smooth Onboarding: With strong processes and a healthy codebase, bringing in new devs got easier. We could grow without sacrificing quality.

And yeah, we still have technical debt. We even have a “tech debt bucket” in Jira. But it’s not this ominous, growing monster anymore. It’s a manageable list. A conscious decision, not a side effect of bad habits.

Wrapping Up

Code reviews aren’t magic. They won’t solve everything. But done right, they can become the heartbeat of a team that cares about quality, about learning, and about building something that lasts.

That’s how we did it. That’s how we’re still doing it.

Related Articles

·

May 27, 2026

What AI Can and Can’t Replace in Design Systems

What happens when you build a design system from v0, Figma, and Windsurf, and let AI handle the speed while you keep the judgment.

12 read time

Read more

Just this month, I built a full design system in about 20 hours.

What used to take weeks, sometimes months, is now dramatically faster. So… what actually changed? And more importantly: what didn’t?

Design systems take time. On complex platforms, they can take hundreds of hours.

We were working with a large and complex product where inconsistencies had started to pile up. Different modules had evolved in isolation, teams were making independent decisions, and there were no shared guidelines. The answer was clear: we needed a design system.

AI tools were just starting to emerge back then. They were mostly useful for simple tasks as they tended to hallucinate when things got complex. Developers had started using them earlier than designers, MCP didn't exist yet, and Figma plugins were the best automation we had.

But the context has changed. Fast.

The Manual Era

We did what most teams did. We stopped, and we built it. Manually.

Picture two designers, a mountain of inconsistencies, and no map. We had to cross-reference information manually, digging through the code, detecting what could be merged, agreeing on naming conventions, deciding how to name components. Hours and hours of discussion until we finally landed on a solution.

In the end, we got there. A cleaner system, faster workflows, and for the first time, both teams speaking the same visual language. Hard-won, but it worked.

But now every month a new AI model seems to be released. Design is finally catching up with what developers faced about two years ago. New tools arose, and with that, the scope of our work as designers completely changed.

The Human Factor

For an internal project, I used our Kaizen site as a reference, combined with documentation from industry leaders as a guideline.

I started in v0, which is essentially a chat interface where you can generate UI components through prompts. I fed it the colors, typographies, and a reference image, and from there it was a back-and-forth: the AI generated, I reacted, adjusted, and pushed until the output matched what I had in my head. And just like that, I started prompting my way through a Design System.

Once a component was ready, I used the html.to.design plugin to bring it into Figma (yes, plugins are still alive!). Think of it as a bridge: the plugin exports designs directly from the browser into a Figma file.

Inside Figma, the intervention was more hands-on. First, I checked that everything was visually consistent with what was defined in v0: colors, typography, styles. Then I used Figma's built-in AI to rename all the component layers using BEM convention (something that would have taken a significant amount of time to do so manually).

BEM, which stands for Block Element Modifier, is a widely adopted naming convention in CSS. It structures layer names hierarchically and predictably, for example: button__label--disabled.

Using it keeps the code clean, readable, and consistent, especially when you're working alongside a developer who needs to understand what came out the other side.

Beyond naming, I also made sure the layer structure would generate the right properties when building component sets in Figma, so that all the variants would be correctly exposed and usable. My team also pointed out that adding descriptions to components and variants was key as context for any agent using them through an MCP.

The last step was connecting everything to Windsurf via MCP. With a frame selected in Dev Mode, Windsurf could read the Figma file and use the components to build more complex screens.

We worked closely with a developer throughout this phase. Not just for the technical knowledge, but because having someone who reads code fluently meant catching things we wouldn't have spotted otherwise. The design role here was direction and supervision: making sure the AI used the components correctly and didn't invent solutions where context was missing.

Every step of the process had a human decision behind it.

AI-assisted UI design workflow showing v0 component generation, html.to.design export to Figma, BEM layer organization, and Windsurf MCP development handoff.

An Unexpected Discovery

At one point, before we had any of the naming conventions figured out, I selected a frame and asked Windsurf to build a form using the components inside it, styled to match a specific card. The developer next to me was skeptical until he saw the result, and then he was just as surprised as I was.

What we realized is that the MCP wasn't reading layer names to understand context. It was reading everything inside the frame, even the loose text sitting alongside the components. Good naming is still worth doing. But the MCP doesn't need it to understand what it's looking at.

UI component library preview with cards, testimonials, service blocks, statistics, and a contact form for a modern software development website.

Learning to Talk to an AI

The more specific and contained your prompt, the better the outcome. We started with the most atomic component: the button, and worked outward from there. Each approved component became context for the next one, so the system gradually picked up the visual language we were building.

At some point I got ambitious and asked for five cards in a single prompt: blog card, service card, testimonial card, stats card, feature card… structures, states and all. The AI delivered.

Visually, everything looked fine. Then the developer looked at the code and pointed out that all five cards were independent components instead of variants of one. For a design system, that breaks everything.

One correction prompt fixed it. But it was a good reminder: the AI does exactly what you ask, not what you mean. And fixing it after the fact can cost more than getting it right from the start.

Some Things Learned Along the Way

  • Precision is key. Natural language is fine when you're asking for a cooking recipe, but when referring to a component, if you say things like "create" instead of "add", you'll probably end up with a whole new set of components instead of additional variants of an existing one.
  • The "Frame" is the context: MCPs can read everything inside the frame you select. This is a game-changer. It means the "naming conventions" debate might be shifting. If the AI understands the context visually and structurally, will we still spend hours discussing nomenclature in 2027?
  • No matter what happens, you can always roll back in less than 5 minutes and start over.
  • Work closely with a developer: they can help you understand MCPs and clear up any code-related doubts. Once you start to grasp their logic, you'll learn very quickly how to prompt in ways that AI actually understands.
  • There's nothing to lose by asking the AI to follow a specific naming convention for the code. It keeps everything clean and readable, and it takes no extra effort.
  • The AI covers roughly 80% of the work (generation, variations, exploration...), but the remaining 20% is where quality lives, and that part is not delegable. The AI executes. The judgment is still yours. And if you skip the review, you're not saving time: you'll spend it later.
  • Context matters more than tooling. What you don't define, the AI will invent. Small components may be resolved well, but large interfaces require more definition from the start. A well-defined system scales. An undefined one generates inconsistencies faster than you can fix them.
  • Figma is no longer the mandatory starting point. It's useful as a visual reference, a QA space, or a consolidation layer. But the AI doesn't need it. We still do.
  • There's no single right workflow yet. What you do depends on the project. We're in a transition moment where the tools change faster than the standards. The best thing you can do right now is experiment.

What AI Still Can’t Replace

Through all of this, a few things became very clear. These are the parts that didn’t change:

  • Knowing when something looks off. The AI generates, but it doesn't notice when the result doesn't feel right. That eye is yours.
  • Direction and supervision. The AI used the components we gave it, but without someone supervising it, it invents solutions where there is no context to work from.
  • The definition of done is still a human call, whether it's a conversation with a PO, a stakeholder, or just the designer's criteria. There's no prompt for that.
  • The context: knowing why certain decisions matter, what a component should communicate, what the user will actually feel. Business knowledge, stakeholder dynamics, unwritten rules, empathy for the end user. These take years to build and live in the people doing the work, not in the tools they use.

My Two Cents

The tools changed, and that gave me the chills, but throughout this experience I found that the designer's role is more alive than ever.

What once took a team weeks can now be prototyped in hours. That’s not a threat; it’s an invitation to get curious.

I'm still figuring a lot of this out, and I suspect most of us are. There's no right workflow yet, and honestly, that's fine. We are in a transition where tools change faster than standards. The best thing you can do is experiment. Don't wait for a "definitive" workflow, it might be obsolete by next month.

Go ahead, try prompting your way through a component. You might be surprised how fast the system starts to take shape.

·

May 15, 2026

Can AI Safely Apply Changes Across Microservices?

AI can update microservices safely, but only when it understands the system’s architecture, ownership, and service relationships.

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Applying changes across microservices is difficult because business logic is distributed across multiple services, each with its own data, contracts, and responsibilities.

In our experiment at Kaizen Softworks, we tested whether an AI system could safely apply coordinated changes across a microservices architecture using only minimal input.

Short answer: Yes, but only when the AI has enough architectural context.

Why are coordinated changes in microservices so hard?

In distributed systems, a single business change rarely affects just one service.

It often requires:

  • Updating multiple microservices
  • Modifying message contracts
  • Keeping DTOs (Data Transfer Objects) consistent
  • Respecting domain boundaries defined by Domain-Driven Design (DDD)

Key entities in this system:

  • Microservice: An independently deployable service responsible for a specific domain
  • Aggregate (DDD): A cluster of domain objects treated as a single unit
  • DTO (Data Transfer Object): A structured format used to transfer data between services
  • Message/Event: A communication mechanism between services

The complexity is not in the code, it’s in the relationships between components.

The experiment: Can AI reason across services with minimal input?

We designed a controlled experiment to test whether an AI model could apply system-wide changes with limited information.

Input given to the AI:

  • Message definitions (events between services)
  • DTOs (data contracts)

Tasks the AI had to perform:

  1. Identify affected aggregates
  2. Determine service ownership
  3. Apply coordinated changes across services
  4. Maintain consistency in messages and DTOs

In other words, the AI had to behave like a software architect, not just a code generator.

What was the biggest obstacle?

The biggest challenge was not technical, it was contextual.

Before and after diagram showing how ambiguous microservice names prevent AI from understanding service ownership, while aggregate-to-service mapping helps AI apply safe coordinated changes.

Problem: unclear service naming

Instead of descriptive names like:

  • order-service
  • billing-service

Our services were named:

  • john
  • sally
  • roger

This removed any semantic clues about responsibility.

Result: The AI could not infer which service owned which domain logic.

The missing piece: aggregate ownership mapping

To solve this, we introduced a simple but powerful structure:

Aggregate → Service mapping

  • Order → john
  • Shipment → sally
  • Invoice → roger

This created a clear relationship between domain concepts and system components.

Once ownership was explicit, the architecture became understandable.

How we used AI to generate architectural context

Instead of building this mapping manually, we used AI to analyze the codebase and extract:

  • Where each aggregate was defined
  • Which microservice implemented it
  • The relationship between domain and infrastructure

The result was a machine-readable architecture map.

In practice, we used AI to generate the context that AI itself needed.

Results: Can AI safely apply distributed changes?

With the architecture map in place, the AI was able to:

  • Trace message flows across services
  • Identify affected aggregates
  • Locate the correct microservices
  • Apply coordinated updates
  • Maintain consistency between DTOs and messages

While not perfect, the system worked reliably as a proof of concept.

What is the real limitation of AI in microservices?

The main limitation of AI is not code generation, it’s architectural understanding.

Without knowing:

  • Which components exist
  • How they relate
  • Who owns what

AI cannot safely modify a distributed system.

AI performance depends more on context quality than model capability.

When can AI safely modify microservices?

AI works well when:

  • Aggregate ownership is clearly defined
  • Message contracts are explicit
  • Architecture is structured and consistent

AI struggles when:

  • Naming is ambiguous
  • Relationships are implicit
  • Context is incomplete

Simple rule: If the architecture is clear, AI can reason. If not, it guesses.

Final thoughts

This experiment revealed something important:

AI doesn’t fail because it can’t write code.
It fails because it can’t see the system.

As teams move toward AI-assisted development, the focus will likely shift from:

Writing better code to Designing better systems for machines to understand

At Kaizen Softworks, we see this as a foundational shift.

Because when AI can understand architecture, it doesn’t just generate code, it helps evolve systems.