> Sounds nice except that these are 1 very small scale model, 1 reranker, and 1 embedding model that are far from frontier LLM level.

We've tried to take a first-principles approach to our end goal of 'legal superintelligence' that has involved identifying the areas of our domain most in need of improvement and releasing models that raise the bar on quality in those areas.

We've been around for a couple months now and ended up starting with retrieval and enrichment. The models we've released to tackle those problems have indeed been smaller in size than their competitors, yet they still rank ahead on open-source benchmarks.

Them being so small also helps with their accessibility — as I mention in our post, our models can be deployed on ordinary hardware, not a supercomputer.

Next on our roadmap is reasoning and research, which will require more infrastructure to support, but again, we aim to be judged by performance at the time of release.

> As much as I agree with the message, this reads like marketing copy trying to make a big deal out of a tiny model being hosted privately.

The point of this point is really just to reaffirm our commitment to sovereignty and accessibility and contrast our approach with that of major AI labs. It _is_ possible to commercialize LLMs while still keeping them accessible. A customer using a self-hosted deployment today does not need to worry about our models no longer being available tomorrow. We think that's a good thing. And moving forward, we want to keep that option available for anything we do, instead of trying to pull up the ladder while we're ahead.