Enabling global companies to translate 20x more on the same budget, making their customer and employee experience instantly multilingual

San Francisco, CA
Joined March 2016
The 2026 State of Translation Automation is live today, and this year it's an interactive website, not a PDF. Choose your translation requirements, a target language and what to optimize for (quality, cost or latency), and see which solutions should work for you.
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Inside: which solutions meet Basic, Standard and Advanced requirements in each language, how prompts, glossaries, quality checks and repair change quality, cost and speed, and where automated quality assessment is reliable and where the evidence is limited.
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We evaluated 229 different solutions built from 57 AI models and MT engines, translating from English into 100 languages against 10 requirements in three levels of translation quality. The report: inten.to/state-of-translatio…
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After you specify your requirements, the next problem is making automatic translation meet them. For each language we use a solution complexity ladder: start with the simplest solution, often the fastest and cheapest, and add complexity and context until it meets them.
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One level above are two-step workflows, usually NMT with an APE step for the context NMT can't handle, or an LLM with an extra step against hallucinations. Multi-step comes next: in the benchmark, a translation step, an LQA gate and a bounded repair of the LQA findings.
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When requirements don't fit into a single prompt or need complex trade-offs, we use multi-agent systems, where AI agents that each own a set of requirements cooperate. We used them for gender neutrality. On Oct 13 we publish what each level takes in price and latency.
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Automating translation now means building a solution that meets your requirements, instead of choosing the "less wrong" off-the-shelf model. Building the harness around the models is iterative, every iteration needs evals, and one black-box 0–1 score can't do that job.
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In the 2026 State of Translation Automation we evaluate against 10 requirements in three levels, as we do for clients. Basic: general translation quality, MQM-based LQA minus subjective requirements. Standard: terminology, tags, formality, formatting, Unicode.
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Advanced requirements like gender neutrality may drive a translation away from the most accurate and fluent one, so the LQA has to understand which MQM violations they make necessary. On Oct 13 we publish what each level takes in price, latency and harness complexity.
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In 2018 there were 18 MT vendors to choose a model from. Today 105 vendors of MT and LLMs supply the building blocks of a translation solution, and in the 2026 State of Translation Automation we benchmark 57 models from 37 of them. We publish the report on 13 October.
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Our new Genesys app translates chat and email, and it can check a reply for offensive or harmful content before the support agent sends it. inten.to/language-hub-for-ge…
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1/ After the study of what people said about the localization job at LocWorld55, we went through what employers do, the open in-house positions and the people they appointed over the summer. The full analysis and the list of positions that are still open are in this thread.
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3/ Four in ten appointees came from outside localization. Most director seats went to insiders. Engineering is one record in fifteen. Europe has the largest share of the jobs. One posting in four is remote.
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We've published Intento Translation Portal for ServiceNow in the ServiceNow Store. Your employees can translate text and documents in 650+ languages inside the Service Portal, with AI checks against company translation requirements. Launch details below.
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A translation can break the rules in your brief and still outscore the correct version on COMET. On locale formatting that happens 36 to 47% of the time, in all four languages. On gender neutrality in German and Spanish, more often than not.
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