Contact center voice AI that fits your stack
Speech-to-text, text-to-speech, voice agents, and audio intelligence for the platforms and teams powering contact centers.
Leading voice teams build with Deepgram
Infrastructure that lets contact centers talk
Listen, speak, act, and analyze on one platform, in real time or after the call.
Deflection and self-service
Answer routine calls with AI voice agents
An assisted contact costs a median $13.50 against $1.84 for self-service. Deepgram Voice Agent API enables you to build AI agents that handle routine calls, with barge-in, turn-taking, and function calling built in. A Fortune 50 retail pharmacy powers over a million calls a day on Deepgram across 7,000 locations.
Route in under 700ms
Replace the IVR tree with intelligent call routing
A misrouted call costs $12 to $15, and 74% of customers hang up on hold. Deepgram's Audio Intelligence enables intent-based routing using what callers actually say, rather than forcing them through a menu. Accurate transcription helps ensure caller intent is correctly understood.
Streaming, sub-300ms
Real-time transcripts guide the live agent
Streaming transcripts arrive under 300ms, fast enough to surface the next best action mid-call. Reddy uses Deepgram to power live agent assist, training, and QA for 5,000 agents, lifting issue resolution per hour 14%, cutting average handle time 9%, and reducing attrition 25%.
QA, coaching, and reporting
Turn calls into reportable data
Transcribe and score the full call volume instead of a sampled few. Deepgram Batch Diarization V2 cut median confusion error rate roughly 80% on contact center audio.
Text-to-speech for agents
Give the agent a voice
Deepgram Flux TTS starts speaking in as little as 80ms and speaks the whole conversation, so tone holds across turns. Deepgram Aura-2 covers narration and batch. Both run on the same streaming infrastructure as speech-to-text, which keeps pronunciation consistent between what the agent hears and says.
Cloud, on-prem, in-network
Self-hosted, cloud, or in-network deployment
Run the same models in our cloud, your VPC, on-prem, or inside a carrier network. Telnyx embedded Deepgram Flux STT inside their network perimeter rather than routing audio over the public internet. Syllable moved to self-hosted and reported error rates and connection resets falling to zero.
What changes when the speech layer is right
Deepgram Nova-3 and Flux STT, measured on 8kHz telephony audio with overlap, accents, and alphanumerics.
- 5.26%Nova-3 median batch word error rate
- 1M+Calls a day at a Fortune 50 retail pharmacy
- 7.3xCost gap, assisted contact versus self-service
What accuracy, latency, and compliance actually mean
How Deepgram performs on the specifics behind the contact center stack.
Built for 8kHz audio
Telephony codecs cost only about 0.6 word error rate points. Narrowband alone isn't why contact center transcripts break.
Overlap is the real problem
Word error rate goes from 3.80% at no overlap to 34.29% at 40% overlap.
Keyterm prompting
Pass up to 100 account terms, product names, and acronyms per request. No retraining required.
800ms is the design center
Mouth-to-ear budgets hold to about 1,200ms and break down past 1,300ms.
Turn detection built in
Deepgram Flux STT knows when a caller has actually finished speaking, so the agent stops talking over them.
One pipeline, one hop
Speech-to-text, text-to-speech, and orchestration run on the same infrastructure, which removes handoffs between vendors.
Redaction before storage
Detect and redact PII from transcripts before they reach reviewers, warehouses, or downstream tools.
PCI, HIPAA, and SOC 2
Gradient Labs ships financial voice support on Deepgram with SOC 2 and EU data residency.
Your audio stays yours
Zero data retention is available, and self-hosting keeps recordings inside your own environment.
FAQ
It is the speech layer a contact center runs on: speech-to-text to hear the caller, text-to-speech to answer, orchestration to manage the conversation, and audio intelligence to analyze it afterward. Deepgram provides all four through one voice AI platform.
That is what Deepgram tunes for. The codec costs about 0.6 word error rate points, which is minor. Overlapping speech is the real problem: error climbs from 3.80% to 34.29% as overlap goes from none to 40%. Diarization and speaker separation are where accuracy is won.
CCaaS platforms run the contact center. Deepgram is the voice layer inside them. If you operate a contact center, ask your platform vendor which speech engine they use. If you are building the platform, or replacing its voice layer, Deepgram is where to start.
Yes. The same models run in your VPC, on-prem, or inside a carrier network. Telnyx embedded Flux STT inside their network perimeter instead of routing audio over the public internet, and Syllable moved to self-hosted, dropping error rates and connection resets to zero.
Around 800ms mouth-to-ear is the design center. Budgets hold to roughly 1,200ms and break down past 1,300ms, while human turn gaps cluster near 200ms. Streaming transcription runs under 300ms, which leaves room for your LLM and text-to-speech inside the budget.
Yes, through audio intelligence: sentiment, intent, topics, summaries, and entities.
Test it on your calls
Run your hardest recordings through Nova-3 and Flux STT, in the cloud or inside your own environment.
See Deepgram in action
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