How to Make a Product Scalable in the Era of New AI Models

Every digital product originates from a concrete need, a specific request we strive to address as quickly as possible. Over time, however, we often realize that this request is not isolated, but instead represents a recurring pattern that can be replicated.

The real question, therefore, is no longer simply: “How do we build this solution?” It becomes: “How do we make it scalable?”

In the era of new AI models, this distinction has become central. While writing code is undoubtedly faster today, building a system that can withstand time, evolve consistently, and be reused across different contexts requires a far more structured approach.

At Fyonda, this is exactly where we are focusing our efforts: using new AI models not merely to accelerate code generation, but to build products with solid foundations.

_New AI Models for a New Workflow

We had already experimented with AI as a development support tool. The early signals were encouraging, but the workflow was not yet reliable enough. Rapid prototyping was possible, yet transforming those prototypes into complete, stable, and truly deployable applications still required significant manual intervention.

With the arrival of Claude 4.5 and Claude 4.6, the shift became evident. This was not simply an incremental improvement, but a concrete change of pace.

Claude 4.5 dramatically increased the speed at which articulated features can be developed. Tasks that previously required days can now be structured in just a few hours.

Claude 4.6, while maintaining the same general approach, introduced an additional level of coherence, reducing inaccuracies, improving contextual awareness, and increasing attention to detail.

The difference, however, is not purely technical. We are moving from “writing code with AI” to building software by guiding AI, and this transition fundamentally changes the quality of the final outcome.

When the model becomes sufficiently reliable, energy is no longer absorbed by raw programming activities; instead, it shifts toward architectural design, meticulous testing, and systematic fixing.

Beyond the Models: The Tools

AI-native editors such as Cursor allow developers to manage workflows through different operating modes, integrated browsing, and advanced context management.

Tools like Thumbstack significantly reduce the time required to locate components and files within complex or multilingual projects, where a simple search string is often insufficient. The minutes saved each day accumulate over time into substantial gains in productivity.

This is an often underestimated aspect of efficiency: it is not only about how quickly you write code, but how quickly you can navigate complexity and intervene precisely where needed.

_Designing Before Building

One of the most common mistakes when working with AI is requesting implementation directly. Although this approach may seem effective in the short term, in the medium term it tends to generate disorder, inconsistencies, and fragmented interventions. For this reason, we have adopted a simple principle that can be summarized as: plan first, write later.

The plan mode enables us to generate a complete technical analysis before touching the code. This is not merely a task list, but a structured architectural evaluation: which parts of the system will be involved, what dependencies exist, whether migrations are required, and which edge cases must be considered.

What once required a manually written functional and technical analysis document is now constructed together with AI. However, it is never passively accepted, it is reviewed, discussed, and refined. This step significantly reduces the risk of accumulating successive patches and helps maintain architectural coherence over time. A scalable product is the result of deliberate decisions, not continuous corrections.

The ask mode complements this approach by allowing us to reason without modifying the code, explore alternative solutions, and challenge the AI to adopt a more critical stance. AI models tend to align too easily with proposed solutions; actively prompting them to suggest alternatives considerably improves the overall quality of the result.

_Intelligent Automation: A System That Updates Itself

Scalability concerns not only architecture, but also operations. A product becomes truly scalable when it does not require constant manual intervention to remain consistent.

A concrete example is the multilingual management of dynamic content. This is not a matter of simply “adding two flags” and translating a few strings, but of ensuring that every modification to products, categories, or information is propagated consistently across all available languages. If each update requires manual action, bottlenecks inevitably emerge, operational time increases, and inconsistencies multiply.

In this context, we designed a system in which every time content is created or modified, a call is triggered to an AI model that automatically generates translations for all languages configured as public. The result is a cleaner workflow, fewer repetitive activities, and a system that remains aligned as it grows.

To integrate this functionality across the entire application, we used a larger model (Claude Opus 4.6 Max) because greater context and precision were required. This choice is also interesting from a cost perspective: investing more to obtain a more robust output becomes reasonable when the complexity of the feature demands it.

Selecting the right model is not a matter of trend; it is a design decision.

_Documentation as Infrastructure

When AI operates directly on the codebase, documentation changes role and becomes part of the infrastructure itself.

An agent working with incomplete context risks generating modifications that appear plausible but are incorrect, or ignoring fundamental conventions.

For this reason, we update documentation during development, not after completion and not days later. Maintaining documentation in real time preserves coherence, reduces errors, and simplifies future interventions, both for developers and for AI systems.

At the same time, document organization becomes increasingly important. In more complex projects, it is advisable to modularize documentation and separate functional documentation (how the product behaves) from technical documentation (how it is structured internally), enabling both developers and AI systems to access only the relevant information.

The goal is not to write more documentation, but to write better documentation, because a scalable product is one that remains understandable even months later.

_The Role of the Developer in the AI-Driven Era

We are entering a phase in which development will increasingly be AI-driven. This does not diminish the role of the developer; rather, it transforms it. Developers increasingly act as architects, supervisors, and guardians of the final quality of a digital project.

AI models tend to validate proposed solutions, which makes it essential to request alternatives, encourage critical analysis, and question the choices they generate.

AI is accelerating rapidly, but direction remains a human responsibility. From now on, competitive advantage will not belong to those who write code faster, but to those who design coherent systems and make sound structural decisions.

If building software becomes simpler for everyone, differentiation will no longer lie in implementation capacity, but in the ability to distinguish and position products strategically. New tools tend to standardize patterns, interfaces, and solutions; without clear direction, products inevitably begin to resemble one another.

For this reason, alongside technical expertise, we increasingly integrate technical vision with product strategy, aiming to build solutions with unique value, clear positioning, and a defined strategic direction. Technology accelerates. Strategy differentiates. And this is the foundation on which we are building our approach at Fyonda.