LanguageOps
I've had the opportunity to spend the last 12 months working on a translation SaaS that does everything the $50k+ incumbents do but also automated QA and LQA, A/V work, smart LLM translation vs millisecond TM and TB matching (memory and termbase files, often gb in size). That is a bit of a paradox, as humans need QA checks (missing punctuation, double spacing, unused termbase term) far more than LLMs yet we are still transitioning and require full human oversight just like in any other field. For quality work, of course. LLMs still need correcting many times anyway, even if we feed a load of context like previous translations, terms, style guides. We try to squash the LLM-ese, much like translation-ese has been a problem historically. But thankfully when the output is tightly defined by the source text, we don't have many LLM-isms creeping in compared to free form responses or prose. Retaining voice and meaning would be the main thing. Assuming the source has one! All good translations need a good source -- you'd be surprised what some think is a good source text. I've been working with a few test clients and have proven workflows for humans alongside the machines to handle files with millions of words, intelligently propagating similar segments, using tokens where required only, compatible with all major tools... I didn't expect to get so far in a year if I'm honest. What I consider to be best practice AI dev has of course been the major driver. Particularly since last November. Including the ability to work in terminal on my phone from anywhere, often late into night or first thing in morning where a laptop would typically not be feasible for me. Best practice in my case would be using a secure, batteries-included framework (you can guess which) reading, documenting, planning everything, keeping a context tree updated fwiw, running frequent performance and security audits, real life testing with a high volume, high scale translation shop working for FAANG and Fortune. I've held out on putting a team together while testing, but I have one ready and might well be able to bring in capital if I can figure out the gap to bridge legacy agencies and new clients in terms of distribution. Or we continue to bootstrap and see how much leverage I can get from automation, pSEO, distribution channels and ads. Please take a look at: https://languageops.com