Call quality scoring (QA)
AI speech analytics that transcribes, scores quality, and detects complaints and risk across 100% of your calls — in your own private cloud.
What Is Call Quality Scoring (QA)?
Call quality scoring — commonly referred to as contact center QA — is the systematic process of evaluating recorded customer conversations against defined performance, service, and compliance standards. For decades, the practical ceiling on this work was set by human capacity: supervisors could realistically review only a small random sample of calls, leaving the vast majority of interactions unexamined and leaving potential compliance failures, unresolved complaints, and coaching opportunities invisible. That sampling gap is not a minor inefficiency; it means that the calls carrying the highest risk are statistically just as likely to go unreviewed as any routine interaction. Modern AI-powered QA closes that gap entirely by automatically transcribing, diarising, and scoring every single conversation as it is recorded, flagging complaints, negative sentiment, and compliance risk without waiting for a supervisor to manually select a call. Allmaz brings this capability to Azerbaijan and the wider CIS region through a solution engineered specifically for the linguistic and regulatory realities of local contact centers. The platform's speech-to-text engine is purpose-built for Azerbaijani and is designed to handle the natural code-switching between Azerbaijani and Russian that characterises everyday agent-customer dialogue in the region — a pattern that generic, globally trained engines consistently struggle with. Critically, the entire solution operates within a single-tenant private cloud environment, meaning every call recording, transcript, and quality score remains inside your own infrastructure at all times. The result is complete call coverage, locally accurate transcription, and data sovereignty — delivered through a single integrated QA platform.
Key Benefits of AI-Powered Call Quality Scoring
Complete call coverage — every inbound and outbound conversation is analysed automatically, eliminating the blind spots created by random sampling and ensuring no non-compliant or high-risk interaction goes unnoticed.
Faster issue detection — complaints and negative sentiment are identified and surfaced in real time, giving supervisors the opportunity to intervene and resolve problems before they escalate into formal disputes or regulatory incidents.
Consistent, objective scoring — AI-driven evaluation applies the same criteria to every call regardless of reviewer fatigue, shift patterns, or individual subjectivity, producing quality scores that are genuinely comparable across agents, teams, and time periods.
Auditable compliance confidence — systematic monitoring across 100% of calls generates a structured, searchable record of every interaction, supporting internal audit processes and providing documented evidence of compliance activity when required.
Local language accuracy — the purpose-built Azerbaijani speech recognition engine handles the mixed Azerbaijani-Russian speech patterns common in regional contact centers, delivering transcript quality that generic engines cannot match in this linguistic environment.
Full data sovereignty — single-tenant private cloud deployment means call audio, transcripts, and derived scoring data are processed and stored exclusively within your own infrastructure, with no data egress to shared or external systems at any stage.
Core Features of Allmaz Call Quality Scoring
100% Call Analysis
Every inbound and outbound call is transcribed and scored automatically — no sampling, no blind spots. This gives managers a complete picture of agent performance and customer experience across the entire operation.
Transcription and Speaker Diarisation
The platform converts speech to text and separates each speaker's turns, producing clean, structured transcripts that make downstream analysis, search, and audit straightforward.
Complaint and Risk Detection
Semantic AI models scan every transcript for complaint signals, negative sentiment, and compliance risk indicators, surfacing the conversations that need human attention first.
Hybrid QA Scoring Engine
Scoring combines rule-based checks (e.g. mandatory phrases, forbidden words) with semantic AI understanding and a human override layer, so quality standards remain precise, explainable, and adjustable.
Purpose-Built Azerbaijani Speech-to-Text
The speech recognition engine is trained specifically for Azerbaijani and handles natural code-switching between Azerbaijani and Russian — a common pattern in local contact centers that generic models handle poorly.
Single-Tenant Private Cloud
The entire solution runs in a dedicated environment on your infrastructure or a private cloud of your choice. No call audio, transcript, or scoring data is transmitted to shared external systems.
How Allmaz Call Quality Scoring Works
Frequently Asked Questions
Does the system really analyse every call, or is it still based on sampling?
It analyses 100% of calls. Unlike traditional QA workflows that rely on random sampling, the AI engine processes every recorded conversation automatically, ensuring no interaction — however brief or routine — is overlooked. This means high-risk calls are just as certain to be reviewed as any other, rather than depending on chance selection.
How does the platform handle Azerbaijani and Russian mixed speech?
The speech-to-text engine is purpose-built for Azerbaijani and is specifically designed to handle the natural mixing of Azerbaijani and Russian that occurs throughout many local contact center conversations. Generic speech engines are typically trained on monolingual data and produce significantly lower transcription accuracy when speakers switch between these two languages mid-sentence — a pattern the Allmaz engine is built to manage correctly.
What does 'hybrid QA scoring' mean in practice?
Hybrid scoring means the platform combines three complementary layers: deterministic rule-based checks that verify whether specific required phrases were spoken or prohibited language was used; semantic AI that understands intent and context beyond simple keyword matching; and a human override capability that allows supervisors to correct or adjust scores when needed. This combination keeps scoring both flexible enough to capture nuanced interactions and auditable enough to satisfy compliance requirements.
Where is our call data stored and processed?
Everything runs in a single-tenant private cloud — a dedicated environment allocated solely to your organisation. No call audio, transcript, or derived scoring data is sent to shared infrastructure or external third-party systems at any point in the process. Your data remains entirely within the boundaries of your own environment from ingestion through to reporting.
Can the scoring criteria be customised to match our internal quality standards?
Yes. The rule-based layer can be configured with your specific scripts, required disclosures, compliance obligations, and quality criteria. The semantic AI layer can be guided by your own definitions of what constitutes a complaint, a risk event, or a service failure. Human overrides applied by supervisors are also fed back into the system over time, continuously refining scoring behaviour to align more closely with your organisation's standards.
Ready to Score Every Call — Not Just a Sample?
See how Allmaz call quality scoring can give your team complete visibility across 100% of conversations, in Azerbaijani and Russian, within your own private cloud. Get in touch with the Allmaz team to arrange a demonstration.
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