I assure you, my knowledge of the French language goes beyond Bonjour, thanks partly to a French-immersion education in elementary school and, more recently, because of our launch in Québec ⚜️🎉⚜️.
Launching Jiffy in Montréal is an amazing milestone for the team. It marks our availability in another major Canadian city, and one of its largest. Montréal is one of Canada’s oldest cities, and its housing landscape is diverse. It’s not just single-family homes but also low-rises, mid-rises and condos, which together create a lot of opportunity in this market. Montréal is also a major hub for our parent company, Intact Financial Corporation, who acquired us in November 2024.
There’s no shortage of reasons to be excited to expand into such an important part of the country 🇨🇦.
Before Montréal, Jiffy operated in English only. Entering a bilingual city meant revisiting many English-first decisions embedded throughout our platform. We also knew we had to get this right because language and culture preservation matter deeply in Québec, and we wanted to feel natural and local. Delivering a fully localized experience wasn’t optional. It needed to feel like it belonged.
It was a monumental task, and we needed to stay within our budget and headcount while targeting a Q4 2025 launch. We began in May and translated more than 200,000 words across the platform in six months by using CrowdIn, AI pre-translation and one human proofreader.
If you’ve localized a product, you’ve probably seen this workflow:
This approach works for a small number of screens, and there’s a reason it became the traditional method, but it collapses when you’re dealing with multiple surfaces, versions and content types. It becomes hard to manage, hard to track and frustratingly slow.
We knew this wasn’t going to scale for us. We adopted it briefly while evaluating options, but the screenshots from that phase make it obvious how quickly it becomes unmanageable.
Translations using the traditional method and a Google Doc. A lot of markup, comments and clarifications in the margins can make it confusing for everyone involved
With the rise of AI, our first instinct was to use an LLM like ChatGPT to translate content directly. It didn’t take long to see the limitations. The translations didn’t sound Québec French, and often leaned toward European French or felt slightly mechanical. Fear not humans, you are still valuable.
The key was having a native Québécois French speaker on the team who could tell us whether translations felt natural. After testing real production copy in ChatGPT and reviewing it with our proofreader, it became clear that AI alone would not be enough.
This led us to explore Translation Management Systems (TMS). These platforms combine AI with structured workflows and tools that improve quality and consistency. The space includes CrowdIn, Phrase, SmartCat and others, and while each one has strengths, they tend to share several features: