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Call-center speech analytics for Telecom

Call-center speech analytics for telecom. Operators handle millions of subscriber interactions across Azerbaijani and Russian, under service-quality SLAs.

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Speech Analytics Built for Telecom Contact Centers

Telecom operators in Azerbaijan manage millions of subscriber calls every month, spanning Azerbaijani and Russian, under strict service-quality SLAs — yet most quality-assurance programs review only a small sample of those interactions. That sampling gap leaves compliance risks undetected, churn signals unaddressed, and agent performance gaps invisible until they surface as subscriber complaints or regulatory findings. Allmaz delivers call-center speech analytics that transcribes, diarises, and scores every single conversation, giving operations and QA teams complete, unfiltered visibility into the full contact-center volume rather than an approximation of it. The platform is purpose-built for the linguistic and operational realities of Azerbaijani telecom: a dedicated speech-to-text engine handles the mixed Azerbaijani-Russian conversations that are routine in local contact centers, where general-purpose engines produce unreliable transcripts. A hybrid QA scoring model combines configurable rule-based checks, semantic AI evaluation, and human reviewer override to produce consistent, auditable quality records aligned to your specific SLA definitions. Because the entire platform runs in a single-tenant private cloud with no data egress, sensitive subscriber data stays within your controlled infrastructure boundary — satisfying data-residency requirements without compromising analytical depth.

Capabilities

Why Telecom Operators Choose Allmaz

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100% call coverage with no sampling ceiling — every subscriber interaction is transcribed, diarised, and scored at full contact-center volume, eliminating the blind spots that sampled QA programs leave in compliance and churn detection.

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Purpose-built Azerbaijani and Russian speech recognition trained specifically for mixed AZ/RU conversations, delivering accurate transcripts where general-purpose engines fragment or fail on mid-call language switches.

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Automatic detection of complaints, escalating language, and negative sentiment across every call, enabling supervisors to prioritize callbacks faster and reduce SLA breaches before they compound.

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Compliance risk flagging that automatically tags and queues calls containing regulatory or policy risk indicators, cutting the manual effort required to meet service-quality obligations and audit requirements.

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Hybrid QA scoring that combines configurable rule-based criteria, semantic AI assessment, and human reviewer override, producing scores that reflect both policy compliance and genuine conversation quality in a consistent, auditable framework.

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Single-tenant private cloud deployment with no data egress, ensuring subscriber call audio and transcripts remain within your infrastructure boundary and satisfy data-residency and privacy requirements.

Core Capabilities

Full-Volume Transcription and Diarisation

Every call is automatically transcribed and speaker-separated — agent and subscriber — regardless of volume. No sampling means no blind spots in your quality or compliance picture.

Purpose-Built AZ/RU Speech Recognition

The speech-to-text engine is designed specifically for Azerbaijani and handles the mixed Azerbaijani-Russian conversations common in local contact centers, delivering accurate transcripts where general-purpose engines struggle.

Complaint and Sentiment Detection

Semantic AI models identify negative sentiment, escalating language, and complaint patterns in real time, allowing supervisors to prioritize callbacks and prevent churn before it happens.

Hybrid QA Scoring

Each conversation receives a quality score derived from configurable rule-based criteria, semantic AI evaluation, and optional human reviewer override — giving QA teams a consistent, auditable framework that adapts to your SLA definitions.

Compliance Risk Flagging

Calls containing regulatory or policy risk indicators are automatically tagged and queued for review, reducing the manual effort required to meet service-quality obligations.

Private Single-Tenant Deployment

The platform runs in a dedicated single-tenant private cloud environment. Subscriber conversation data never leaves your infrastructure boundary, satisfying data-residency and privacy requirements.

How It Works

1Call audio is ingested directly from your contact-center telephony infrastructure into the single-tenant private cloud environment in real time or batch.

2The purpose-built Azerbaijani speech-to-text engine transcribes each call and diarises the conversation into agent and subscriber turns, handling mixed AZ/RU speech accurately.

3Semantic AI models and rule-based engines analyze every transcript simultaneously, scoring conversation quality, detecting complaints, flagging negative sentiment, and identifying compliance risk.

4Each call receives a hybrid QA score combining automated rule checks, AI semantic assessment, and any human reviewer override, producing a consistent and auditable quality record.

5Dashboards surface aggregated trends — complaint volumes, churn-risk signals, SLA adherence rates, and agent performance — so operations managers and QA leads can act on the full picture rather than a sample.

6Flagged calls and low-scoring interactions are automatically routed to the appropriate supervisor or compliance queue, closing the loop between detection and resolution.

Frequently Asked Questions

Does the platform truly analyze every call, or is there a practical volume ceiling?

The platform is architected to analyze 100% of calls at telecom-scale volumes with no built-in sampling limit. Capacity is provisioned to match your contact-center throughput, so the coverage guarantee holds regardless of daily or seasonal call spikes.

How does the system handle conversations that switch between Azerbaijani and Russian mid-call?

The speech recognition engine is purpose-built for Azerbaijani and trained specifically on mixed AZ/RU conversations, which are routine in local subscriber interactions. It produces a single coherent transcript across language switches rather than fragmenting or defaulting to one language, which is a common failure mode in general-purpose engines applied to this market.

Where is subscriber call data stored, and who can access it?

The deployment is single-tenant and runs in a private cloud environment you control. No call audio, transcript, or derived data is routed through shared infrastructure or external third-party services. This architecture is designed to satisfy data-residency obligations and subscriber privacy requirements from the ground up.

Can our QA team adjust scoring criteria to match our specific SLA definitions?

Yes. The hybrid QA scoring framework includes configurable rule-based criteria that your QA team can map directly to your SLA thresholds and compliance requirements. Human reviewers can also override automated scores on individual calls, and those overrides are recorded in the audit trail and can inform ongoing calibration of the scoring model.

What does the onboarding process look like, and how quickly can we expect actionable insights?

Allmaz works with your team through a structured onboarding process that covers telephony integration, scoring rule configuration, and transcription quality validation before go-live. The exact timeline depends on your infrastructure environment and the complexity of your QA framework; our team provides a realistic estimate during the initial discovery phase so you have a clear picture before committing resources.

See What 100% Call Coverage Reveals

Talk to the Allmaz team about deploying speech analytics across your contact center — purpose-built for Azerbaijani telecom operations, with full data privacy and no sampling compromises.