Automate call QA
Automate call QA with Stentor: a practical, on-prem approach built for Azerbaijani teams.
Automate Call QA: Review Every Conversation, Not Just a Sample
Quality assurance teams have traditionally been limited by a fundamental constraint: the volume of calls far exceeds the capacity of human reviewers. This reliance on random sampling creates dangerous blind spots, where critical compliance failures or customer complaints go unnoticed simply because they weren't selected for review. Stentor, developed by Allmaz, eliminates this gap by transcribing, diarising, and scoring 100% of your conversations automatically. By moving from statistical sampling to total coverage, your organization gains a complete, auditable record of every interaction. Designed specifically for the local market, Stentor features a purpose-built Azerbaijani speech-to-text engine that accurately handles the mixed Azerbaijani and Russian conversations typical of regional contact centres. To ensure maximum security and regulatory compliance, the entire system operates within a single-tenant private cloud, ensuring that no data ever leaves your controlled environment. The result is a scalable, high-precision QA framework that transforms raw audio into actionable intelligence without compromising data privacy.
Strategic Advantages for QA Teams
Eliminate Blind Spots: By analysing 100% of calls instead of random samples, you ensure that no high-risk interaction goes unreviewed.
Rapid Risk Mitigation: Automated detection of complaints, negative sentiment, and compliance risks allows supervisors to intervene and resolve issues in real-time.
Localized Linguistic Precision: Purpose-built Azerbaijani speech-to-text handles natural AZ/RU code-switching, reducing the need for manual transcription corrections.
Absolute Data Sovereignty: Single-tenant private cloud deployment ensures that call audio and transcripts remain on your infrastructure with no data egress.
Auditable Scoring Accuracy: A hybrid approach combining rule-based checks, semantic AI, and human overrides ensures scores are consistent, explainable, and fair.
Optimized Resource Allocation: Analysts stop wasting time on routine spot-checks and instead focus their expertise on flagged calls and complex coaching cases.
Core Capabilities of Stentor
100% Call Coverage
Every conversation is processed—not a statistical sample. This means no high-risk call goes unreviewed simply because it was not drawn in a random selection.
Transcription and Speaker Diarisation
Stentor converts audio to text and labels each speaker turn, giving reviewers a clean, readable record of who said what and when.
Azerbaijani-First Speech Recognition
The speech-to-text engine is purpose-built for Azerbaijani and handles naturally occurring code-switching between Azerbaijani and Russian—the reality of most local contact centres.
Automated Complaint and Sentiment Detection
The system flags calls containing complaints, negative sentiment, or language patterns associated with compliance risk, surfacing them for priority human review.
Hybrid QA Scoring
Scores are produced by combining rule-based criteria (e.g. required phrases, forbidden language) with semantic AI understanding, and supervisors can apply human overrides to any score.
Private Cloud Deployment
Stentor runs in a single-tenant environment on your infrastructure. Call recordings, transcripts, and scores are never transmitted to external services.
The Workflow: From Audio to QA Score
Frequently Asked Questions
Does Stentor replace human QA analysts?
No. Stentor automates the tedious transcription, scoring, and triage work. This allows analysts to focus their time on high-value tasks that require human judgement, such as resolving complex complaints, handling borderline compliance cases, and conducting agent coaching.
How does the system handle mixed Azerbaijani and Russian speech?
Unlike generic multilingual models, our speech-to-text engine is purpose-built for the local environment. It is specifically designed to handle the natural code-switching between Azerbaijani and Russian that occurs frequently in Azerbaijani contact centres.
Where is our call data stored and processed?
Security is central to our architecture. Everything runs in a single-tenant private cloud deployed on your own infrastructure. No audio files, transcripts, or metadata are ever transmitted to external cloud services or third-party platforms.
Can we customise the QA scoring criteria?
Yes. The hybrid scoring model is flexible, combining rule-based checks—which you define based on your specific KPIs—with semantic AI evaluation. Additionally, supervisors maintain full control via manual overrides for any automated score.
What exactly is 'hybrid QA scoring'?
Hybrid scoring uses three layers for maximum accuracy: rule-based checks verify objective requirements (like mandatory disclosures), semantic AI evaluates the overall meaning and tone, and human override allows supervisors to correct or annotate scores for a fully auditable process.
Ready to Review Every Call, Not Just a Sample?
Talk to the Allmaz team about deploying Stentor in your contact centre. We will walk you through a practical setup that fits your existing infrastructure and compliance requirements.
Request a demo