AI can generate a web page quickly, but in real work the first draft is not the end of the process.
After generation, teams still need to review the structure, rewrite copy, improve CTA wording, adjust colors, inspect sections, and prepare the page for sharing or delivery.
We just launched Katalyst for sales teams on Salesforce.
Simple idea: reps don't hate selling, they hate the hours they lose every week feeding the CRM. Logging calls, fixing stages and close dates, writing next steps, reconstructing what happened on a deal from three weeks ago. The CRM they "quietly hate."
Most tools just made data entry slightly less painful. It's still the rep doing the work.
Localization Engine Suggestions are concrete edits to a localization engine a glossary entry, an instruction, or a brand-voice change proposed by the platform from its own AI Reviewer verdicts.
When reviews on a translation come back low, the platform suggests a specific edit to the localization engine's glossary, instructions, or brand voice, with the reasoning attached. Apply writes the edit into the localization engine and the next translation runs with the fix in place. An auto-suggestions toggle on the localization engine's Reviews tab runs the analysis in the background, so a run of low scores quietly produces suggestions waiting in the Suggestions tab. Suggestions are also addressable from code through the Localization Engine Suggestions API https://lingo.dev/docs/api/engin... free-text feedback like the German copy sounds too stiff goes in the same way and returns the same kind of structured suggestion.
