Introducing Vantage: a no-code workspace for localizing marketing and long-form content
Purpose-built for campaigns and documents, embedded in the systems and tools marketing teams already use.
Product & News
Introducing Vantage: a no-code workspace for localizing marketing and long-form content
Purpose-built for campaigns and documents, embedded in the systems and tools marketing teams already use.
Sahil Gambhir·Updated on September 16, 2026
How to get data-backed proof that AI translation works — on your own content
Here's a stat that should make every localization leader uncomfortable: 57% of localization teams say their number one barrier to using AI more is that they don't trust the quality. Not that the quality is bad. That they don't trust it. The distinction matters. AI translation has improved dramatically. Models are better, context-aware systems like
AI translation quality evaluation: Looking beyond human review
The meeting is going well. Translation costs are down, turnaround times are shorter, and AI is taking on more of the work. Then your VP asks a question: “How do you know the quality of AI translations is good enough?” Most localization teams can confidently measure speed and cost, but measuring quality is far less straightforward. Reviews, spot checks, and a handful of examples help, but they don't provide a consistent basis for deciding where
How to localize Figma designs using Figma's AI agent and Lokalise MCP
Figma Config changed the localization game for designers. Here’s how to connect Figma’s native AI agent to Lokalise via MCP — and manage translations without leaving your canvas. Something significant shifted at Figma Config. Figma introduced a native AI agent built directly into the app — and it can connect to external tools through MCP (Model Context Protocol, an open standard that lets AI assistants talk to other software). For designers who localize
How Lokalise integrates with your enterprise tech stack: Salesforce, CMS, DAM, and API
Enterprise localization doesn't fail because of bad translations. It fails because tools don't talk to each other. Content sits in a CMS. Code lives in GitHub. Sales teams work in Salesforce. Support runs on Zendesk. Designers are in Figma. When the localization platform can't connect to all of them, someone ends up exporting files, emailing them around, and importing them back. That's where errors, delays, and missed launches come from. Lokalise
Best TMS platforms for developers: API, CLI, and SDK support compared
Developer-focused translation management systems (TMS) are designed to fit into existing engineering workflows. Instead of relying on manual exports, imports, and handoffs, they help teams automate localization through APIs, version control, CI/CD pipelines, and SDKs. This guide compares the best TMS platforms for developers, based on what actually matters in practice:
MCP for localization: How to connect AI agents to your translation workflow
You're in Cursor, working on a new feature, and you need to add a localization key. That means leaving your IDE, opening your TMS, navigating to the right project, making the change, and coming back. Then, doing this all over again the next time you need to check untranslated strings, create a task, or touch anything localization-related. Model Context Protocol (MCP) removes this context-switching loop entirely. Instead of bouncing between tools, your AI coding assistant can inter
MCP vs REST API for Localization: When to Use Each
Lokalise gives developers two programmatic ways to manage localization workflows: the REST API and the MCP Server. In any MCP vs API localization decision, the key point is that one does not replace the other. The REST API is built for scripted, repeatable, deterministic automation, such as CI/CD pipelines, batch imports, and webhook-driven deployments.
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