Hopper

Founded by Pavan Katta

Run fast voice inference by training models directly on your live call traffic

Pricing model

Paid

Rating

★ People love it

Time to value

Requires Expertise

You can use Hopper to run ultra-low latency voice inference for speech-to-text, text-to-speech, and conversational language models. The service trains these voice models on your real-world production calls and serves them directly against active customer traffic. By utilizing custom inference kernels and hardware tuning, it delivers time-to-first-token latencies as low as eighty milliseconds. This allows engineering teams to drastically cut down audio response lag in voice agents while lowering overall compute costs compared to standard hosted cloud models across customer support lines.

What Hopper does

Train speech-to-text models on production audio logsServe low-latency text-to-speech audio streamsMeasure time-to-first-token response speedsDeploy fine-tuned speech models to live call trafficBenchmark voice agent costs against standard LLM providersExecute custom inference kernels on specialized hardwareSub-100 millisecond time-to-first-token latency for voice interactionsCustom GPU megakernels tuned for fast throughputContinuous model retraining using live production call dataCombined inference pipeline covering STT, LLMs, and TTSSignificantly lower input token pricing than frontier commercial modelsHardware-level kernel tuning across modern graphics processors