Missed names and numbers on noisy calls broke workflows. Now, AI accurately transcribes live speech, routing complex cases to humans.
A conversational-agent platform deployed across 23 markets and over 100 enterprise teams automates complex customer interactions for regulated industries.
Live phone conversations involve challenging audio conditions like 8 kHz telephony, background noise, and varied accents. If a speech recognition...
“Voice is the oldest interface we have. Enterprises are now rediscovering it as the richest modality for customer trust, but only if every layer of the stack is best-in-class. Deepgram is a core part of how we deliver that for our English-language deployments, and we’re partnering closely on expanding that into the languages our European customers operate in.”
Hybrid human-AI platform for automating complex customer experience workflows.
Voice AI platform for speech-to-text, text-to-speech, and conversational agents.
GetVocal AI's Voice automation is part of this use case:
Related implementations across industries and use cases
Building custom speech infrastructure risked draining engineering. An API-first voice layer refocused developers on core orchestration.
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Without emotional warmth, users felt self-conscious during personal AI chats. Expressive text-to-speech made conversations feel human.
Building custom speech infrastructure risked draining engineering. An API-first voice layer refocused developers on core orchestration.
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Missed names and numbers on noisy calls broke workflows. Now, AI accurately transcribes live speech, routing complex cases to humans.
A conversational-agent platform deployed across 23 markets and over 100 enterprise teams automates complex customer interactions for regulated industries.
Live phone conversations involve challenging audio conditions like 8 kHz telephony, background noise, and varied accents. If a speech recognition...
“Voice is the oldest interface we have. Enterprises are now rediscovering it as the richest modality for customer trust, but only if every layer of the stack is best-in-class. Deepgram is a core part of how we deliver that for our English-language deployments, and we’re partnering closely on expanding that into the languages our European customers operate in.”
Hybrid human-AI platform for automating complex customer experience workflows.
Voice AI platform for speech-to-text, text-to-speech, and conversational agents.
GetVocal AI's Voice automation is part of this use case:
Related implementations across industries and use cases
Building custom speech infrastructure risked draining engineering. An API-first voice layer refocused developers on core orchestration.
Dense API docs bottlenecked non-technical users. Now, Claude builds fully configured voice agents from simple plain-text requests.
Without emotional warmth, users felt self-conscious during personal AI chats. Expressive text-to-speech made conversations feel human.
Building custom speech infrastructure risked draining engineering. An API-first voice layer refocused developers on core orchestration.
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.