The speech-to-text layer behind QA tools that score every call

Most STT providers benchmark on aggregate WER. Your QA scorecard depends on whether the right specific words landed – compliance phrases, account numbers, required disclosures. Gladia is built for keyword-level accuracy, not the metric that looks good in a benchmark.

• Low keyword error rate on structured entities
• Named entity recognition
• Compliance phrase accuracy on noisy telephony audio
• Account numbers, contract IDs, and contact data captured correctly

Manual QA programs review a fraction of calls because the economics of their transcription layer made full coverage impossible. Gladia's async pipeline is priced and built for scoring every interaction your contact center produces, same-day.

• Priced for full-volume async
• Same-day call availability for QA review
• All QA features (diarization, sentiment, NER, keyword flagging)
• No manual sampling ceiling

Every QA output – talk ratios, per-agent sentiment, compliance attribution – depends on correctly knowing who said what. Gladia delivers 3x fewer diarization errors on real telephony audio, with dual-channel accuracy your scorecards can build on.

• 3x fewer diarization errors
• Dual-channel diarization built for 8kHz telephony
• Per-speaker sentiment and talk ratios
• No per-channel billing surcharge

Gladia returns speaker-labeled transcripts with sentiment scores and named entities — structured for your QA scorecard or compliance dashboard without custom parsing or transcript cleanup.

• Utterance-level sentiment
• Named entity recognition
• Structured output, QA-pipeline-ready
• No separate NLP layer required

Start with async post-call QA. Add real-time coaching triggers, compliance alerts, or keyword detection mid-call later. Same API, same schema, no second provider.

• ~300ms streaming for live coaching
• Same output schema as async
• Real-time keyword detection and compliance alerts
• No second provider needed