Why We Run Our LLM Locally
Local deployment means zero cloud latency, complete data privacy, and lower long-term costs. Here's why we made it the default.
When we started AI Engine Technologies, every voice-AI vendor we evaluated shipped the same architecture: capture audio, ship it to a cloud endpoint, wait, and stream the response back. It works — but it inherits three problems we refused to accept: latency, privacy, and cost.
Latency is a conversation killer
A natural conversation has sub-second gaps. Round-tripping audio to a distant data center adds hundreds of milliseconds before the model even starts thinking. Running the Large Language Model on the same machine that handles the call keeps total response time under 500ms.
Zero cloud latency means instant responses. Speed is not optional.
Privacy is non-negotiable
For healthcare, finance, and government callers, the data cannot leave the building. A local model means sensitive transcripts and context never traverse a third-party network — a prerequisite for HIPAA and SOC 2 compliance.
Cost flattens over time
Cloud inference is priced per token forever. A local GPU is a capital expense that amortizes. Once you handle enough daily interactions, the local path is dramatically cheaper — and you keep full control of the weights and prompts.
The trade-off
Local AI needs real hardware (a modern server, ideally with a GPU) and operational ownership. We help you assess whether that fits — and offer hosted options when it doesn't.
Local-first isn't a feature we bolted on. It's the foundation.