Intento provides a language hub for localization, offering machine translation and generative AI solutions for translations.

Intento, Inc.

Intento

Intento is a machine translation and multilingual generative AI platform for global enterprise companies. Its Enterprise Language Hub enables companies like Procore and Subway to deliver consistent, authentic language experiences across all markets and audiences. It combines machine translation and generative AI models into automatic translation workflows, customizing them to client data and integrating them into customers’ existing software systems for localization, marketing, customer support, and other business functions.


With Intento, clients achieve high-quality, real-time translations for all users and team members worldwide. The Enterprise Language Hub is ISO-27001 certified, ensuring enterprise top-tier security for GenAI solutions in high-demand industries. Intento also offers ISO-9001-certified expert help for setting up and maintaining MT and AI models and constantly refines these models with new data and user feedback.

Anton Antich

Amadeo Global

SkyDeck Berkeley

Flint Capital

SmartHub

Davidovs Venture Collective

Somersault Ventures

I2BF Global Ventures

Courtyard Ventures

Scorpion Capital

🎉 We’ve published our 8th annual State of Machine Translation report. It analyzes 52 MT engines and LLMs across 11 language pairs and 9 content domains. Get your copy here ➡️ https://hubs.la/Q02zD6Z70

Here’re some key findings:
🎯 LLMs reshaped the MT landscape and now account for 55% of top-performing models
🎯 Quality varies by language and domain, with LLMs excelling in Colloquial, Education, and Entertainment
🎯 Traditional Machine Translation (MT) still outperforms in specific language pairs and domains
🎯 LLMs are less expensive but slower than MT engines
🎯 Open-source LLMs lag behind commercial offerings
🎯 Customization through translation memories, glossaries, and prompt engineering is key to eliminating errors

Explore the full report to learn more about the evolving capabilities and potential of LLMs.

e2f, inc.

We’ve added the ability to export and import translations to and from Storage in CSV format. This is useful for syncing with other systems like TMS or data warehouses. It’s also great for reviewing translations in Excel or Google Sheets. Simply export the translations you need to a CSV file from our Translation Storage, share it with your reviewers to edit in Excel or Google Sheets, and then import the updated CSV file back. Let’s chat so we can tell you more 👉 https://hubs.ly/Q02L5KMm0

Expanding your global reach? Learn how we used our Website Translator to make our website speak 7 languages (and you can too!). We share helpful tips and lessons on how to make the process easier, keep translations consistent, and avoid common mistakes ➡️ https://hubs.ly/Q02L5p5N0

Google Gemini just took first place on the LMSYS Chatbot Arena Leaderboard for the first time. The new Gemini 1.5 Pro Experimental 0801 model, now in preview, shines in multilingual tasks.

Meta's recently announced Llama 3.1 405b Instruct also made the top 5.

https://hubs.la/Q02JNF_y0

Remember, this doesn't guarantee great performance across all language tasks. Always test models thoroughly for your specific needs. Still, it's exciting to see so many capable models emerging in the field.

Meta announced Llama 3.1, a family of three models with 8B, 70B, and 405B parameters. The context size is expanded to 128k tokens, and the models are multilingual by design. The largest 405B model is competitive with leading foundation models, including GPT-4, GPT-4o, and Claude 3.5 Sonnet, across a range of public benchmarks. We are eager to test it on our multilingual use cases!

https://hubs.la/Q02HF66T0

OpenAI announced GPT-4o mini, a fast and small model that surpasses good old GPT-3.5 Turbo. GPT-4o mini has improved multilingual understanding over GPT-3.5 Turbo across a wide range of non-English languages. It is also more than 60% cheaper!

As usual, the model is instantly available for machine translation across all Intento connectors and integrations! Try in on your tasks today!

https://hubs.la/Q02HdBx60

We’ve upgraded our Translation Storage to help you maintain accurate translations across various use cases like real-time chats, knowledge bases, user-generated content, and more! Contact us to see it in action https://hubs.la/Q02GVB1x0.

We added a segment-level MT Configuration analysis so you can review individual segments to see the exact combination of machine translation engines, glossaries, and other settings used during the translation process. This granular insight enables you to quickly pinpoint the root cause of quality issues.

You can now make fixes that apply to all future translations, not just an isolated segment. Simply navigate to the problematic segment in our Translation Storage, adjust the MT configuration or terminology, and the changes will be applied across all use cases using this segment.

Patronus AI released a hallucination detection LLM called Lynx. It is built on Llama-3 and does well on public hallucination evaluation benchmarks, outperforming GPT-4o and Claude-3-Sonnet on some of them. Lynx can be a useful tool to mitigate hallucinations in LLMs; however, it requires careful evaluation on your tasks.

https://hubs.la/Q02GMq3b0

You can now import your Translation Memory (TMX) files into our Translation Storage to leverage it in all Intento plugins and integrations, including real-time translation use cases like live chats, on-the-fly website translation, and more. Book a demo to see how it works! ➡️ https://hubs.la/Q02FmrzJ0

While we initially developed Translation Storage to review and reuse past machine translations, it now serves as a data bridge between content and experience translation.

Last week, many sources reported that Google Translate now supports 110 new languages (https://hubs.la/Q02F0frH0). The whole list is here: https://hubs.la/Q02F05YJ0.

Our systems automatically monitor API changes, but we didn't receive any advance notice. The reason is apparent if you read the original post carefully—these languages were added to the Google Translate web and mobile application, but they aren't powered by Google NMT; instead, they come from PaLM 2, one of Google's LLMs.

This raises an interesting point in the debate about LLMs' ability to handle both popular and low-resource languages. They may excel at both ends of the spectrum but struggle with languages in between. For widely-spoken languages, LLMs outperform NMT by using more data. With low-resource languages, where NMT lacks sufficient training data, LLMs produce decent results due to their highly multilingual nature. For less popular languages and domains, achieving substantial quality requires dedicated evaluation and data curation efforts, which we may not see immediately. See our “The State of Machine Translation 2024” report for insights: https://hubs.la/Q02F06rr0

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