More enterprises — especially in finance, healthcare, legal, and government — are moving away from public cloud AI. Even though modern APIs offer strong privacy protections, internal policies are shifting toward full data control, where any external processing is a risk by default.
Local LLMs are the answer. They run entirely on a company’s own servers, with no data leaving the corporate environment.
What changes for localization?
When enterprises adopt strict internal AI policies, they expect every piece of data — including translation memories, glossaries, and sensitive project content — to stay inside their infrastructure. That makes typical AI-powered localization workflows harder to apply and puts pressure on LSPs to adapt. The challenge is finding a solution that allows AI to run on a company’s own servers within CAT tools.
What’s next?
Fully local setups aren’t yet standardized enough to replace cloud workflows everywhere. But hybrid approaches — local processing for sensitive projects, cloud for everything else — are emerging as the practical middle ground.
Tools like Ollama, vLLM, LM Studio, and TensorRT-LLM are making local LLM deployment a practical option. Proprietary solutions — including AI-powered localization tools — are now enabling direct integration with local models by connecting to platforms like these.
Our approach
Hera AI will soon support local LLMs, allowing AI-powered processing to run entirely inside a client’s infrastructure when needed, with cloud-based workflows for other cases.
