Voice AI

For voice agents where the caller hears every millisecond.

Every 100ms of latency is user churn

Every model call sits in the loop between hearing and speaking, and the caller hears all of it as a pause. Each 100 milliseconds you add is felt, and calls that stall get abandoned.

Cost still matters at call volume, but a response that arrives late is a failed task no matter how cheap the tokens were.

Faster models do not speak like a human would

The obvious fix is a smaller off-the-shelf model. It answers sooner and stops sounding like your agent: it misses the intent, drops the detail the caller just gave, and falls back on filler.

Staying on a frontier API doesn't fix the tail either. You share queues with everyone else's traffic, you can't pick the region the request lands in, and prompt work doesn't move the p99.

Small models are fast and accurate

Fast and accurate stop competing once the model is trained for one task. We fine-tune an SLM on the bounded jobs inside your pipeline, so it holds your accuracy bar at a fraction of the parameters.

Time to first token falls with the parameter count, and we serve it where your stack runs: a regionalized endpoint, your VPC, or your own hardware. The p99 becomes yours to control, and cost per call falls with it.

Where it applies

Find out what your workload should cost

We identify where you are overspending, evaluate the optimizations available, and show you the lowest-cost configuration that meets your quality bar. It starts with an export or one day of traffic.