Turn speech into analytics-ready data
Speech-to-text, diarization, and audio intelligence APIs for analytics platforms, in streaming or batch.
- Routes by intent
- Summaries your users read
- Cloud, VPC, or on-prem
Powering analytics platforms in production
The conversation is captured. Now see where it can take you.
Deliver conversation analytics to your contact center customers, in real time or after the call.
Post-call, at full volume
Ship QA that reviews every call
Your QA product is only as good as the transcript under it. Abby Connect's team reviews 5x more calls per day and cuts per-call audit time 30% on 100,000+ calls a month. Redaction strips PII before transcripts reach reviewers, and 2X Solutions AI runs it as a TCPA control.
Streaming, sub-300ms
Coach the agent while the customer talks
Real-time coaching needs transcripts that arrive while the customer is still talking. Streaming transcription runs under 300ms, fast enough to prompt an agent in the moment. Deepgram Flux STT adds turn detection when your product needs to know the caller actually stopped speaking.
Intent and topics
Audio intelligence for intent-based routing
Group calls by what the customer actually wanted. Intent recognition and topic detection can turn a volume spike into a cause. Intent accuracy is gated by transcription: past roughly 10% word error rate, classification degrades measurably.
Customer sentiment, post-call
Measure the customer experience
Sentiment analysis scores how a customer experienced the call, turn by turn and overall.
Who said what
Get speaker attribution right
Every analytics metric that splits agent from customer depends on diarization. Batch Diarization V2 was preferred 3.3x over the previous model in human evaluation, and cut median confusion error rate by roughly 80% on contact center audio. Talk-time ratios and per-speaker sentiment inherit that.
Cloud, VPC, or on-prem
Deploy where the recordings live
Some customers can’t let call recordings leave their environment. Run the same models self-hosted, in your VPC, or on-prem, and resell them as part of your platform. Syllable moved to self-hosted and reported error rates and connection resets falling to zero.
The numbers that hold up under testing
Deepgram Nova-3 and Batch Diarization V2, measured on the audio contact centers actually produce.
- 5.26%Nova-3 median batch word error rate
- ~80%Reduction in median confusion error rate, Batch Diarization V2
- 40-46 ptsTerminology recognition gain from keyterm prompting
Accuracy and intelligence that scale
From transcription accuracy under real-world conditions to structured insights and predictable infrastructure costs, Deepgram gives speech analytics teams the foundation to build on and the numbers to back it up.
Accuracy sets the ceiling
CallTrackingMetrics had 40% of transcriptions too inaccurate for reliable analytics before Deepgram.
3.80% to 34.29% on overlap
Word error rate goes from 3.80% at no overlap to 34.29% at 40% overlap.
Keyterm prompting
Pass up to 100 product names, competitor names, and acronyms per request, no retraining.
Sentiment, intent, and topics
Audio Intelligence returns structured fields from the same request that returns the transcript.
Redacted before reviewers see it
Extract structured fields for reporting, and redact PII before transcripts reach your customers’ reviewers.
Summaries your users read
Call summaries give supervisors context without replaying audio or reading a full transcript.
Stop maintaining Whisper
SupervizeAi built Whisper on Kubernetes twice before switching. Their CEO wanted out of the DevOps business.
Throughput for archives
Process years of stored recordings in batch. AWL had a decade of recordings sitting unprocessed in S3.
Live and post-call results match
Streaming and batch share models and features, so your real-time and post-call numbers agree.
FAQ
A speech analytics API is the speech-to-text and intelligence layer underneath a speech analytics product. Deepgram transcribes the call, labels the speakers, and returns sentiment, intent, topics, and entities as structured fields. You build the dashboards, scoring, and workflows your customers buy.
Conversation intelligence platforms are finished products sold to contact centers. Deepgram is the layer underneath them. If you're buying a dashboard, buy one of theirs. If you're building the dashboard, or embedding analytics in a product you sell, start here.
Every downstream metric inherits its errors. A missed product name breaks keyword tracking, a mislabeled speaker corrupts talk-time ratios, and a garbled sentence skews sentiment. Named-entity recognition alone drops 20 to 27 F1 points on transcribed speech versus clean text.
Yes. The same models run self-hosted in your cloud, your VPC, or on-prem, and you can resell them inside your platform. That is how vendors serve customers whose call recordings cannot leave their environment, including PCI, HIPAA, and EU data residency requirements.
Codec loss is the smaller issue. G.711 costs about 0.6 word error rate points. Overlapping speech is what breaks analytics: error rate climbs from 3.80% to 34.29% as overlap goes from none to 40%. Diarization and speaker separation are where the accuracy is won.
Yes, and it is a growing pattern. Teams that shipped a voice agent then need post-call and in-call intelligence on it: containment rate, where callers dropped, which intents the agent mishandled. The same APIs cover human and agent conversations.
Test it on your audio
Run your customers’ hardest recordings through Nova-3 and compare the transcript to what you ship today.
See Deepgram in action
Loading form