Speech & contact-centre AI
Global speech engines often return fluent Turkish when given Azerbaijani audio. Our speech recognition is built for Azerbaijani from the ground up, trained on real phone calls, and powers analytics that review every call instead of a sample.
Request a pilot on your dataThe problem
1–3%
Manual QA samples a tiny share of conversations; complaints and compliance breaches go unheard.
Wrong language
Global engines output plausible text in a neighbouring language — nobody notices until it reaches a report.
Studio audio
Engines trained on read-aloud recordings break on noise, interruptions and phone-line quality.
Per hour
Cloud transcription charges by the hour and keeps its own copy of your customers’ conversations.
What you get
Speech-to-text designed for Azerbaijani and mixed AZ/RU speech, trained on genuine call-centre audio.
Every call transcribed, split by speaker and scored — with complaints, negative sentiment and compliance risk surfaced automatically.
How it works
Deployment & trust
Single-tenant, on your own infrastructure: audio never goes to a third party, nothing is retained outside your network, and there is no metering by the hour.
Who it’s for
Service quality, complaint trends and risky departments at a glance.
Compare teams and agents and find bottlenecks in one view.
Review only the calls that matter; validate or override AI scores.
Search risky phrases, export evidence and review the audit trail.
In oil, gas & energy
Transcribe and search shift handovers and operational communications.
Quality and complaint monitoring for energy retail, gas distribution and service lines.
Azerbaijani transcripts of HSE investigations, interviews and meetings, kept on-prem.
FAQ
Yes — a pilot runs on a sample of your real recordings so you can judge the Azerbaijani output before committing.
Yes. Conversations that switch between Azerbaijani and Russian are common in call centres and are part of the training data.
Through recording exports or an API integration with your contact-centre platform; we scope this in discovery.
Related
Cited answers from your documents and SOPs, on an Azerbaijani-native language model inside your network.
Pressure-test AI assistants with synthetic users, run autonomous regression QA, and pentest behind the login.
Not sure where to start? A fixed-scope discovery sprint maps your use cases first.
A pilot runs on your documents, calls or records — measured against criteria we agree up front.
