Kvadrat is a Danish, family-run business supplying high-quality textile solutions. Founded in 1968 by two friends, the company began in a backyard with the ambition to create textiles of exceptional quality. Originally focused on upholstery, Kvadrat has since expanded to offer curtains, textiles, rugs, acoustic solutions, roller blinds, and even tabletops made from textile waste.
Renowned for pushing the boundaries of design, Kvadrat has collaborated with some of the world’s most celebrated art institutions and exhibitions; including MoMA, the Guggenheim, and the Venice Biennale.
Kvadrat is proud of its strong Scandinavian roots and design identity – an identity that must be preserved across all markets and languages.
We sat down with Virginia Bisconti, Kvadrat’s Content Marketing Specialist and main point of contact for LanguageWire, to learn how Kvadrat solved one of their biggest localisation challenges: automating localisation while staying in full control of tone and brand in every language.
“We have a very strong identity, tone of voice, and specific way of communicating that is rooted in the Scandinavian standards.”
She shared her thoughts on how LanguageWire’s Own Your Workflow model made this possible.
The challenge: Fragmented processes and limited control
Before LanguageWire, Kvadrat translated their material via agencies and freelancers. This created fragmented workflows, time-consuming back-and-forth, and the burden of coordinating multiple stakeholders. As a result, timelines, efficiency, and quality suffered.
Working with many different agencies and freelancers meant there was no unified process. Each group needed detailed briefs, which were not always understood or followed correctly. As a result, it was difficult to achieve consistent results.
Translating Kvadrat’s understated, humble Scandinavian communication style consistently required significant context, training, and brand knowledge, especially in markets where that tone is not common. In the United States, for example, big, bold statements are the norm. If Kvadrat wanted to avoid that style, they had to train each freelancer to an extent that was simply unsustainable.
“A lot can get lost in translation, so it's important for us to control that part.”
Using multiple external agencies also created another challenge: ownership of accumulated knowledge. Information such as what had already been translated, why certain translation choices were made, approved terminology, and the ongoing learnings behind those decisions often remained within the agencies, leaving Kvadrat without long-term control.
For a global company where brand voice is essential, the setup was not working. Kvadrat needed a centralised, efficient localisation solution that not only automated workflows but also gave them full ownership of output and linguistic data.
"We wanted our tone of voice to trickle down to all the languages, so all of a sudden we are not too enthusiastic in French or too bold in Spanish.”
The solution: Automated localisation with full brand control
Kvadrat wanted a provider that could offer the right translation technology, was well established in the industry, and had a clear roadmap for the future.
LanguageWire ticked those boxes.
LanguageWire’s Own Your Workflow solution helped Kvadrat automate their localisation workflows. LanguageWire handled all operational tasks, while Kvadrat kept full control of brand quality by including their own in-country experts in the localisation workflow. Kvadrat’s marketing team remained responsible for reviewing and approving all content to ensure the brand’s tone and identity were preserved in every market.
Delegating project management and workflow automation to LanguageWire freed Kvadrat to focus on what matters most: brand-driven content.
“The project managers are always on top of everything and super helpful when it comes to switching deadlines or putting pressure on our validators.”
Integrating LanguageWire’s Translation Management System (TMS) directly with Kvadrat’s Optimizely CMS allows the team to order translations without leaving their own platform. The centralised setup replaced emails and manual coordination with a single hub.
As Kvadrat expanded into Asian markets, AI also became a valuable tool. AI-generated drafts helped bridge cultural context gaps, allowing validators to refine content rather than starting from scratch.
AI has opened the door to languages and markets that would be harder to reach using only human translators. Humans need context, cultural background, and detailed briefings to capture a specific communication style. With AI, you can train the model once and simply refine the output through internal reviewers. It removes pain points where specific terminology or communication styles wouldn’t immediately make sense to someone outside the culture.
Kvadrat uses native English copywriters because a western context is familiar to them. But for their Japanese and Korean content, for example, AI helps bridge the gap by producing output that’s already close to the intended meaning and tone.
Validators then review and fine-tune the content. Explaining all that context from scratch to a human translator each time would be slow and complex.
“Having AI in place is really a game changer. If you had to explain all the context to a human translator, that would take way too long at times.”
The result: Reliable, scalable, and transparent
After implementing the solution, ordering translations became faster, simpler, and more intuitive. LanguageWire’s solution has sped up the process, centralised operations, and given Kvadrat greater control without requiring time-consuming tools and processes. The solution also supported Kvadrat’s shift towards a more strategic approach to localisation, prioritising what content to translate and when based on business value. As a result, cost control improved significantly, with a 14% cost saving achieved between 2024 and 2025.
“From 2024 to 2025, we achieved around 14% cost savings.”
Translation quality has also improved thanks to features like Termbase and Translation Memories.
For some languages, achieving the same level of quality is more complex. Most large language models are still trained primarily on English. They perform well in languages widely used online. Even big languages with billions of speakers (like Japanese or Korean) lack data. That’s why success requires an intentional setup. It’s about teaching the MT engine with the right data, building a robust termbase with preferred terminology, and supporting validators with solid processes.
“I cannot wish for anything better than the current team that we have.”
Instead of treating the workflow as a one-off setup, LanguageWire and Kvadrat collaborate to refine instructions, processes, and validator journeys on an ongoing basis. For example, new guidelines were recently introduced to steer validators through the review process exactly as intended by the Kvadrat marketing team.
Overall, Virginia praises LanguageWire’s reliability, transparency, and ability to give Kvadrat full control of quality while keeping operations simple.
“LanguageWire is reliable and transparent.”
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