# 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.

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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.

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.