Voice + language AI for contact centres, media, multilingual ops
Real-time transcription, voice agents that don't sound robotic, sentiment scoring, language detection. Whisper, Deepgram, ElevenLabs — wired in properly, not "pip install" deep.
Why voice + NLP finally got good
Speech recognition was a 20-year arms race that mostly produced mediocrity. The Whisper / Deepgram / GPT-4o-realtime generation of models broke that — accuracy is now genuinely production-grade for most languages, and latency is low enough that real-time voice agents are viable for the first time.
Most companies haven’t caught up. Contact centres still pay for clunky 2018-era IVR. Media companies still pay humans to transcribe podcasts. Operations teams still skip multilingual coverage because it was historically expensive. All of that is now solvable in weeks, not quarters.
What production voice / NLP ships with
The capabilities that turn a Whisper demo into a system a contact-centre or media company can run on.
Real-time STT
Streaming transcription with sub-300ms latency. Word-level confidence scores. Speaker diarisation built in.
Natural-sounding TTS
Voice cloning, accent control, emotion shaping. Output good enough that users don't flinch.
Multi-language
60+ languages with auto-detection. Particularly strong on UAE / UK / India / South-East Asia language mix.
Real-time voice agents
Conversational voice agents end-to-end — STT → LLM → TTS — round-trip under 800ms. Phone-grade, not gimmick-grade.
Sentiment + intent
Live or batch sentiment, urgency, intent classification. Pipes into your CRM, CX dashboard, or routing logic.
Auto-summaries
Call / meeting / interview summarisation with action-item extraction. Saves CX teams 30-60 mins per call.
Where voice + NLP earns money
Three patterns where the maths almost always works.
Call analytics + agent assist
- Real-time transcription on every inbound call
- Sentiment + topic tagging in the CRM
- Agent-assist suggestions in their sidebar as the call happens
Podcast / video transcription
- Auto-transcribe at production-grade accuracy
- Generate chapters, summaries, social clips
- Translate captions to 20+ languages cheaply
Voice-based phone agents
- Inbound qualification before human takes the call
- Outbound appointment confirmation, no-show recovery
- Sounds human enough that users complete the call
From audio source to live system
Most voice projects ship in 4-6 weeks. Real-time agents take 6-8 because the latency engineering is real.
Week 1 · Source + benchmark
Test 3-4 STT / TTS providers on your actual audio (accent, noise, jargon). Pick the winner on accuracy + cost + latency.
Weeks 2-3 · Build the pipeline
Streaming or batch ingestion, language detection, diarisation, sentiment. Wire it into your CRM / dashboard.
Week 4 · QA + edge cases
Heavy accents, two people talking over each other, low-quality phone audio. We test on the cases that break naive setups.
Week 5-6 · Launch + tune
Phased rollout. Live monitoring of word-error rate, latency, language coverage. 30 days of tuning included.
What teams ask before going voice-first
Quick answers on the questions that actually matter.
01 How accurate is the transcription, really?
For clean English audio (single speaker, decent mic), word error rate is typically 3-5%. For phone-grade audio with accents, 6-10%. For very noisy multi-speaker calls, 10-15%. We benchmark on YOUR audio before quoting, not on a generic Wall Street Journal dataset.
02 Do voice agents actually sound human now?
For scripted interactions and short conversations, yes — the newest TTS models pass a casual ear test. For long open-ended conversations, the cracks still show. We’re honest about which use cases work today and which still need a human.
03 Can you handle our multilingual customer base?
Yes. Whisper-class models handle 60+ languages, with auto-detection and code-switching (mixing two languages mid-sentence). Particularly strong on UAE-relevant languages — Arabic, English, Hindi, Urdu, Tagalog.
04 How real-time is "real-time"?
Streaming transcription typically lands words in 200-400ms. End-to-end voice agents (STT → LLM → TTS) land in 600-1000ms round-trip on a good network. Slower than a human, but not noticeably so for most callers.
05 What about privacy — these are customer calls?
Same options as our other AI work: vetted APIs with zero-retention contracts, or self-hosted models for the strictest requirements. PII redaction (names, card numbers, account numbers) before storage if you need it.
What teams say after going live with voice & NLP tools
Send us sample audio — we’ll benchmark for you
Two-minute form. We reply within 4 working hours.