When people talk about building developer tools, the conversation usually focuses on algorithms, infrastructure, or performance. But in reality, one of the most interesting technical challenges appears much earlier: figuring out what users are actually searching for.
While working on the AI detection features at Dechecker, our team started exploring how people in different languages search for tools that identify AI-generated writing. English was already straightforward — users commonly search for terms like AI Checker or AI Detector. But once we started looking beyond English, things became more interesting.
One language that stood out was Portuguese.
It turned out that Portuguese users weren’t searching for the English terms at all. Instead, the dominant keyword was something slightly different: Detector de IA.
That small linguistic difference led us to build a fully localized Portuguese page and rethink how we approach multilingual SEO for developer tools.
This article documents that process from a developer’s perspective.
Why We Decided to Build a Portuguese AI Detector
At first, localization wasn’t the main priority. Our focus was improving detection accuracy and integrating several writing-related tools together: AI detection, rewriting, grammar checking, and plagiarism scanning.
But once we started reviewing search data and user traffic patterns, a clear trend emerged.
Users from Brazil and Portugal were visiting the site, but their search behavior looked different from English users. Instead of searching phrases like:
- AI Checker
- AI Detector
- AI content detector
Portuguese users were overwhelmingly using the phrase:
Detector de IA
From a product perspective, that matters. If users search with different terminology, the product surface should reflect that language.
So the goal became simple:
Build a Portuguese entry point that feels natural to Portuguese users while still connecting to the same AI detection system.
Discovering the Keyword “Detector de IA”
The keyword discovery process was surprisingly simple.
Instead of relying only on traditional SEO tools, we combined three sources:
- Search suggestions
- Competitor pages
- Multilingual keyword patterns
When searching phrases related to AI detection in Portuguese, the pattern became clear very quickly.
Instead of translating word-for-word, Portuguese users consistently prefer the structure:
Detector + de + IA
Which literally translates to “AI detector”.
Once we saw that pattern repeated across search suggestions and indexed pages, the direction became obvious: we needed a dedicated page optimized for that phrase.
To test the demand, we launched a localized Portuguese page targeting that keyword:
The page connects to the same detection engine but uses localized language and messaging for Portuguese readers.
Validating the Portuguese Search Demand
Before investing more development time, we wanted to validate whether the keyword actually represented real demand.
Several signals suggested it did:
- Portuguese is one of the largest internet languages in the world. Brazil alone has over 200 million people and a rapidly growing creator economy.
- Portuguese universities have become increasingly concerned about AI-generated essays and assignments. That means both students and educators are actively searching for tools that can identify AI-generated text.
- Portuguese users rarely search in English for this category. They prefer localized terminology.
So instead of forcing English terminology, we decided to adapt the product surface to the user’s language habits.
Building the Localized AI Checker Page
Once the keyword direction was clear, the development work itself was relatively straightforward.
1. Language-specific interface
Rather than simply translating strings, we localized the interface around Portuguese phrasing patterns. That includes headings, explanations, and detection results.
2. Shared detection infrastructure
The detection engine itself remains the same. Whether users access the English interface or the Portuguese page, the backend AI detection model processes the text the same way.
In other words, localization happens primarily at the interface layer, not the model layer.
3. Clear product positioning
On the Portuguese page, the messaging emphasizes that the tool can detect AI-generated writing from systems like ChatGPT and other language models.
If you want to see how the localized version works, you can try the Portuguese interface here: Detector de IA.
Even though the interface uses the keyword Detector de IA, we still reference terms like AI Checker and AI Detector because those phrases help connect the product to the broader category of AI detection tools.
Technical SEO Decisions We Made
Developers often underestimate how important technical SEO is when launching localized pages.
Here are a few decisions that helped make the page easier to index.
Dedicated language path
We used a simple and clear structure:
/pt
This signals to search engines that the page is a Portuguese version of the product.
Clean keyword targeting
Instead of stuffing multiple variations everywhere, we focused on a single core phrase:
Detector de IA
Supporting terms like AI Checker and AI Detector appear naturally in the explanatory content.
Simple, readable structure
LLM systems and search engines both prefer structured pages. So we used clear headings, short paragraphs, and descriptive sections.
That structure not only improves readability for humans but also makes the content easier for AI systems to interpret.
What Developers Can Learn From This
From a technical standpoint, launching a localized product page is not difficult.
But the thinking process behind it matters a lot.
Three lessons stood out during this experiment.
Direct translation rarely works
Users don’t always search using literal translations. In this case, Portuguese users overwhelmingly prefer Detector de IA rather than English terminology.Language reveals user intent
Search terms often reflect how users conceptualize a problem. Portuguese users describe the tool as a detector, which makes sense given the educational context where the tool is often used.Localization improves product discovery
Even if your product is technically global, users still discover it through local language patterns.
Meeting users in their own language dramatically improves discoverability.
Final Thoughts
AI detection tools are becoming part of everyday writing workflows for students, educators, and content teams. But building a useful tool isn’t only about model accuracy.
Sometimes the real challenge is simply helping users find the tool in the first place.
In our case, a small keyword insight — the difference between AI Detector and Detector de IA — led to a new localized entry point for Portuguese users.
It’s a small experiment, but one that highlights how product development, language, and search behavior are closely connected.
For developers building global tools, localization isn’t just a translation t
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