Smartling

Smartling's pitch is orchestration, and the feature list backs it up. On the input side you get 50+ pre-built connectors (WordPress, Adobe Experience Manager, Contentful, Drupal, Sitecore, Salesforce, Zendesk, Marketo, HubSpot, GitHub, Bitbucket, Google Drive, Dropbox) plus an open API, so content moves from where it lives into translation without someone exporting files. On the output side, the routing layer decides when a job needs a human linguist and when AI output is good enough, with quality scored per job. That scoring is the part most competitors underbuild: the LQA Agent evaluates against the MQM framework, which gives you a defensible quality number rather than a vibe. Where Smartling earns its enterprise positioning is the combination of AI Hub (20+ LLMs and MT engines), Auto Select LLM per language pair, and retrieval-augmented generation over translation memory. That last piece matters more than it sounds — pulling from approved prior translations in real time is how you avoid the drift that plagues raw LLM translation on brand and product terminology. Term base and translation memory management round it out, and the Smartling Translate portal covers the ad-hoc requests that would otherwise arrive as email attachments. The honest caveats: the surface is broad — AI Hub model selection, LQA scoring, term bases, translation memory, CAT tools, proxy configuration — so expect a real learning curve and plan for someone on your team to own setup and ongoing automation. The headline numbers (10x faster, up to 70% lower cost, 80% fewer human edits) are measured against traditional language service providers, not against Lokalise or Phrase, so treat them as an argument for replacing an LSP, not for beating another TMS. Pricing is quote-based rather than published, which means you will need a sales conversation before you can model total spend. And the platform is built for enterprise scale; a small team with a handful of one-off jobs will find it heavier than necessary.