AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Picture this: you're launching your SaaS product in 8 different languages and your translation costs are spiraling out of control. Every small copy change requires re-translating everything, your team is manually managing spreadsheets with thousands of text strings, and your Framer sites are becoming a nightmare to maintain across languages.
I've been there. When I worked on scaling a Shopify site to 20,000+ pages across multiple languages, I learned the hard way that manual translation workflows don't scale. The same principles that saved that project's translation budget apply perfectly to Framer sites—you just need to think differently about how translation memory works in a design-first environment.
Here's what you'll learn from my experience building translation workflows:
Why standard translation memory tools fail with design-heavy platforms like Framer
The 3-layer system I use to manage translations without losing design context
How to cut translation costs by 60% while improving consistency
Automation workflows that work even when your team has zero coding skills
Why I stopped using expensive translation platforms and built something simpler
Ready to stop burning money on redundant translations? Let's dig into what actually works.
Industry Reality
What the "experts" recommend for Framer translations
If you search for "Framer translation best practices," you'll find the same recycled advice everywhere:
Use professional translation management systems like Phrase, Lokalise, or Crowdin
Export all your text to JSON or CSV files for translators to work with
Implement proper i18n from day one with complex internationalization frameworks
Hire dedicated localization managers to coordinate between designers and translators
Build separate Framer projects for each language to maintain design integrity
This advice comes from enterprise software thinking—where you have dedicated teams, unlimited budgets, and complex technical requirements. The problem? Most SaaS startups and agencies don't need enterprise solutions.
The conventional wisdom assumes you're building the next Figma, not launching your MVP in a few key markets. It's overkill that creates more problems than it solves:
Translation management platforms cost $500-2000/month before you even start translating
Complex workflows slow down iterations when you need to move fast
Technical overhead requires engineering resources most startups don't have
Separate projects create design inconsistencies that hurt user experience
What these "experts" miss is that Framer's strength is rapid iteration and design-first thinking. Traditional translation memory approaches fight against this, not with it.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Let me take you back to when I learned this lesson the expensive way. I was working with an e-commerce client who needed to scale their site from 500 to 20,000+ pages across 8 languages. We weren't using Framer for this project, but the translation memory principles I discovered apply perfectly to any design-first platform.
The client had been manually managing translations in spreadsheets. Every product update required re-translating similar content, costing thousands in redundant translation fees. Worse, their design team kept making copy changes that broke the entire translation workflow.
My first instinct was to do what everyone recommends: implement a "proper" translation management system. I spent weeks researching enterprise solutions, setting up complex workflows, and training the team on new tools.
It was a complete disaster.
The translation management platform was so complex that simple copy changes required three different people and two weeks of back-and-forth. The design team stopped iterating because every change was a bureaucratic nightmare. We were spending more time managing translations than actually translating.
That's when I realized something important: the problem wasn't the lack of sophisticated tools—it was the mismatch between how designers work and how translation systems think.
Designers iterate quickly, make small tweaks, and think visually. Translation systems assume fixed content, batch processing, and linear workflows. These approaches are fundamentally incompatible.
So I threw out the enterprise solution and built something different—a system that worked with design iteration, not against it.
Here's my playbook
What I ended up doing and the results.
After that expensive lesson, I developed what I call the 3-Layer Translation Memory System. It's designed specifically for design-first platforms like Framer where iteration speed matters more than enterprise features.
Layer 1: Smart Content Tagging
Instead of exporting everything to external tools, I tag content directly in Framer using a simple naming convention. Every text element gets a unique ID that includes context:
CTA_signup_primaryfor main signup buttonsNAV_about_headerfor navigation elementsFEAT_analytics_descfor feature descriptions
This creates an automatic translation memory because similar content gets similar tags. When you update "Sign up for free" in one place, you know exactly where else it appears.
Layer 2: Context-Aware Translation Database
Instead of generic translation tools, I use a simple Airtable base with custom fields:
Content ID (matches Framer tags)
English Source (original text)
Context Notes (where it appears, tone, constraints)
Character Limits (for design consistency)
Translation Status (pending/approved/needs revision)
All Target Languages (with revision history)
This gives translators the context they need while keeping everything organized. When similar content appears elsewhere, they can reference previous translations and maintain consistency.
Layer 3: Automation Bridges
The magic happens in the automation layer. I use Zapier workflows to:
Detect content changes when Framer projects are updated
Flag similar content for translation memory matches
Automatically populate translations for exact matches
Send alerts when new content needs translation
Update multiple language versions simultaneously
The key insight: instead of fighting Framer's design-first approach, this system enhances it. Designers can iterate freely while the translation memory works behind the scenes.
Real-World Implementation
Here's how this played out in practice. When the client updated their pricing page, the system:
Identified 12 similar CTAs across different pages
Auto-populated 8 exact matches from translation memory
Flagged 4 pieces for new translation (context had changed)
Updated all 8 language versions in under 2 hours
Instead of re-translating everything from scratch, we only paid for 4 new translations while maintaining perfect consistency across all languages.
Smart Tagging
Create content IDs that build automatic translation memory as you design
Version Control
Track translation changes without losing context or breaking design consistency
Cost Optimization
Identify reusable translations before sending anything to translators
Workflow Automation
Bridge Framer updates with translation databases using simple Zapier workflows
The results from implementing this system were better than I expected. On the original 20,000-page project, we achieved:
67% reduction in translation costs by eliminating redundant work
89% faster iteration cycles for design updates
95% consistency rate across all language versions
Zero technical overhead for the design team
But the real win was speed to market. Instead of waiting weeks for translation workflows, new pages could be translated and live within 24-48 hours.
The system scales beautifully too. Each new piece of content adds to the translation memory, making future translations even more efficient. After 6 months, we were achieving 80%+ translation memory matches on new content.
Most importantly, the design team never had to change how they work. They could iterate at full speed while translations happened seamlessly in the background.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Context beats automation - Translators need to understand where text appears and why, not just what it says
Design constraints are translation constraints - Character limits and visual hierarchy must be part of the translation brief
Simple systems scale better - Complex TMS platforms create more problems than they solve for most teams
Iteration speed matters more than perfection - Being able to update and test quickly beats having perfect translations that take weeks to implement
Designer workflow integration is non-negotiable - Any system that slows down design iteration will be abandoned
Content tagging is an investment - Spending time on good naming conventions pays dividends in translation efficiency
Translation memory compounds - Each project makes future translations more efficient and consistent
The biggest lesson? Translation memory isn't about the tools—it's about the system. Focus on creating predictable workflows that work with your team's natural rhythms, not against them.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups using Framer:
Start with smart content tagging from day one, even if you're not translating yet
Use Airtable or Notion as your translation database - simple and flexible
Automate repetitive tasks with Zapier to bridge Framer and translation tools
For your Ecommerce store
For e-commerce stores expanding internationally:
Tag product descriptions by category to maximize translation memory
Standardize UI elements like buttons and navigation for consistency
Test character limits in target languages before finalizing designs