Automate call QA
AI speech analytics that transcribes, scores quality, and detects complaints and risk across 100% of your calls — in your own private cloud.
Automate Call QA Across Every Conversation
Quality-assurance teams have long operated under the constraints of random sampling, reviewing only a small fraction of agent interactions while the vast majority of calls go unexamined. This approach leaves complaints undetected, compliance risks buried, and coaching opportunities missed until problems have already escalated. Allmaz's AI-powered call QA solution eliminates that blind spot entirely by transcribing, diarising, and scoring 100% of your calls automatically — surfacing complaints, negative sentiment, and compliance risk in real time rather than days or weeks after the fact.
What Your QA Team Gains
Complete call coverage — every inbound and outbound conversation is transcribed, scored, and reviewed automatically, with no call left unexamined due to sampling limits.
Faster risk response — complaints, escalation signals, and compliance triggers are surfaced in real time, allowing supervisors to act on the calls that matter most rather than discovering issues weeks later.
Consistent, auditable scoring — the hybrid QA engine combines rule-based checks, semantic AI, and human override so every score is explainable, fair, and traceable for internal audits or regulatory review.
Local language accuracy — the purpose-built Azerbaijani speech-to-text engine handles naturally occurring mixed AZ/RU conversations, delivering reliable transcripts that reflect how your agents and customers actually speak.
Full data sovereignty — single-tenant private cloud deployment ensures that no call audio, transcript, or metadata ever leaves your own infrastructure or passes through shared external services.
Reduced analyst workload — by automating routine call review and flagging only the interactions that require human attention, QA analysts can redirect their time toward coaching, edge-case review, and strategic quality improvement.
How the Platform Works
100% Call Transcription and Diarisation
Every inbound and outbound call is automatically transcribed and speaker-separated, giving QA teams a complete, searchable record of every conversation without manual effort.
Automated Quality Scoring
Each call receives a structured quality score derived from a hybrid engine that combines configurable rule-based criteria, semantic AI understanding, and the ability for human reviewers to override any score.
Complaint and Sentiment Detection
The system identifies negative sentiment, escalation signals, and explicit complaint language in real time, allowing supervisors to prioritise follow-up on the calls that matter most.
Compliance Risk Flagging
Calls containing potential compliance risks — such as prohibited phrases, missing disclosures, or regulatory triggers — are automatically tagged so your compliance team can act quickly.
Purpose-Built Azerbaijani Speech Recognition
The speech-to-text engine is built specifically for Azerbaijani and handles naturally occurring code-switching between Azerbaijani and Russian, delivering accurate transcripts for real-world contact centre conversations.
Private Cloud Deployment
The entire platform runs in a single-tenant private cloud environment. No call audio, transcripts, or metadata are sent to shared infrastructure or external services, satisfying strict data residency requirements.
From Call Recording to QA Insight in Four Steps
Frequently Asked Questions
Does the system really review every call, or is it still based on sampling?
The platform analyses 100% of calls without exception. Every conversation is transcribed, speaker-diarised, and scored automatically — there is no sampling threshold or call volume cap that causes interactions to be skipped. This means complaints and compliance issues that would previously fall through the gaps of a sampled review are now consistently detected.
How does the hybrid QA scoring model work?
Scoring is built on three complementary layers. First, rule-based checks that you configure — such as required disclosure phrases, prohibited language, or mandatory call structure steps. Second, a semantic AI layer that interprets the meaning and context of the conversation beyond simple keyword matching. Third, a human override capability that allows your QA analysts to adjust any score with a full audit trail, ensuring the final result is always explainable and defensible.
Our agents switch between Azerbaijani and Russian mid-call. Will transcription still be accurate?
Yes. The speech-to-text engine is purpose-built for Azerbaijani and is specifically designed to handle the mixed AZ/RU code-switching that is common in Azerbaijani contact centres. Rather than forcing calls into a single-language model, the engine processes naturally occurring language transitions, producing accurate and coherent transcripts that reflect how conversations actually unfold.
Where is our call data stored, and who can access it?
The platform is deployed as a single-tenant private cloud within your own infrastructure. No call audio, transcripts, quality scores, or metadata are transmitted to shared environments or third-party services. Access is governed entirely by your own security and access-control policies, giving your organisation full ownership of and accountability for its data.
Do we need to replace our existing telephony or recording system to get started?
No. The platform is designed to ingest audio from your existing call recording infrastructure, so there is no requirement to change your telephony setup or migrate to a new recording system before deployment. Integration works with the recording environment you already have in place.
Ready to Review Every Call, Not Just a Sample?
Talk to the Allmaz team to see how automated call QA can work inside your own private cloud environment.
Request a demo