An alternative to foreign speech analytics
An alternative to foreign speech analytics: a local, on-prem alternative for Azerbaijani business — see how Stentor compares.
The Local Standard for Azerbaijani Speech Analytics
Most speech analytics tools on the market were built for dominant global languages and hosted on infrastructure outside your control. For Azerbaijani businesses, this often results in degraded transcription accuracy—particularly during mixed Azerbaijani-Russian conversations—and mandatory data transfer to foreign servers. These limitations create significant compliance exposure and operational gaps that generic platforms simply cannot resolve. Stentor by Allmaz is a purpose-built, on-premises speech analytics platform engineered specifically for the Azerbaijani market. By combining local language fidelity with a single-tenant private cloud architecture, Stentor provides contact centres and enterprise teams with full conversation coverage and absolute data sovereignty. It transforms raw audio into actionable intelligence without compromising security or linguistic nuance.
Why Azerbaijani Enterprises Choose Stentor
Eliminate sampling gaps by analysing 100% of calls, ensuring no compliance risk or customer complaint goes undetected.
Achieve superior transcription accuracy with a speech-to-text engine purpose-built to handle the real-world mix of Azerbaijani and Russian.
Guarantee total data sovereignty via a single-tenant private cloud deployment with zero data egress to external servers.
Reduce manual monitoring through automated detection of complaints, negative sentiment, and regulatory compliance risks.
Ensure trustworthy quality metrics using hybrid QA scoring that blends rule-based logic, semantic AI, and human override capabilities.
Deploy a solution designed for local regulatory and operational requirements rather than a retrofitted global template.
Core Platform Capabilities
Full-Coverage Call Analysis
Stentor analyses 100% of calls rather than a sampled subset, so no conversation is missed when it comes to quality assurance, compliance monitoring, or customer experience review.
Transcription, Diarisation and Scoring
Every conversation is automatically transcribed, speaker-separated (diarised), and scored — giving supervisors a structured, searchable record of every interaction without manual effort.
Azerbaijani-First Speech Recognition
The speech-to-text engine is purpose-built for Azerbaijani and handles naturally occurring mixed Azerbaijani-Russian speech, a common reality in local contact centres that foreign platforms handle poorly.
Automated Risk and Sentiment Detection
Stentor detects complaints, negative sentiment, and compliance risk in real time across all calls, enabling faster escalation and reducing the chance of issues going unresolved.
Hybrid QA Scoring
Quality assurance scores are produced through a combination of rule-based criteria, semantic AI understanding, and the ability for human reviewers to override — balancing automation with accountability.
Single-Tenant Private Cloud
Stentor runs in a single-tenant private cloud environment with no data egress, meaning your call recordings and transcripts remain entirely within your own infrastructure boundary.
The Stentor Workflow
Frequently Asked Questions
How does Stentor differ from global speech analytics platforms?
Unlike global platforms built for dominant languages and hosted on shared foreign clouds, Stentor is purpose-built for Azerbaijani and mixed Azerbaijani-Russian speech. It operates within a single-tenant private cloud with no data egress, solving the critical language accuracy and data sovereignty issues that foreign tools cannot.
Does the system rely on call sampling for its analysis?
No. Stentor analyses 100% of your calls. While sampling-based approaches leave significant gaps in quality assurance and compliance, our full-coverage model ensures every single conversation is transcribed, scored, and available for audit.
How is data security and privacy handled?
Security is central to the architecture. All processing and storage take place within your own single-tenant private cloud environment. No call recordings, transcripts, or derived metadata ever leave your infrastructure boundary.
What makes the 'Hybrid QA Scoring' approach more effective?
It combines the precision of rule-based criteria (e.g., mandatory phrases) with the contextual understanding of semantic AI. By allowing human reviewers to override these scores, the system balances automated efficiency with human accountability and judgment.
Can the platform accurately process code-switching between Azerbaijani and Russian?
Yes. The speech-to-text engine is specifically engineered to handle the natural mixing of Azerbaijani and Russian common in local business environments, a specific area where generic international engines typically fail.
Ready to See Stentor in Action?
If your current speech analytics tool was not built for Azerbaijani language or your data sovereignty requirements, it may be worth exploring what a purpose-built local alternative looks like. Contact the Allmaz team to arrange a demonstration of Stentor in your environment.
Request a demo