An alternative to foreign speech analytics
AI speech analytics that transcribes, scores quality, and detects complaints and risk across 100% of your calls — in your own private cloud.
A purpose-built speech analytics platform that analyses every call, not just a sample
Most speech analytics platforms were designed for English-first markets, rely on random sampling to keep processing costs manageable, and route your recordings through shared cloud infrastructure operated by third-party vendors. The result is a partial picture: compliance risks go undetected, complaint patterns emerge too late, and QA scores reflect only a fraction of actual agent performance. Allmaz was built from the ground up to solve these problems for Azerbaijani contact centres — transcribing, diarising, and scoring every single conversation, including the mixed Azerbaijani and Russian calls that generic engines routinely mishandle, all without your audio ever leaving your own dedicated environment.
Why contact centres choose Allmaz for speech analytics
Full call coverage with zero sampling — every conversation is transcribed, scored, and checked for risk signals, giving management an accurate picture of performance rather than a statistical estimate
Native Azerbaijani speech recognition trained specifically for the code-switching between Azerbaijani and Russian that is common in real contact centre calls, delivering accurate transcripts where generic engines fail
Automatic detection of complaints, negative sentiment, and compliance risk phrases across 100% of call volume, surfacing issues that would be missed entirely under a sampled approach
Single-tenant private cloud deployment keeps all recordings, transcripts, and scores inside your own dedicated environment with no data egress and no shared infrastructure exposure
Hybrid QA scoring that combines configurable rule-based checks, semantic AI analysis, and human override so results are both consistent at scale and contextually accurate in edge cases
No dependency on foreign vendor infrastructure or cross-border data transfers, giving your compliance and data-governance teams a clear and auditable data residency position
What Allmaz speech analytics actually does
100% Call Transcription & Diarisation
Every call is automatically transcribed and separated by speaker, giving supervisors a complete, searchable record of every conversation without manual effort.
Purpose-Built Azerbaijani Speech-to-Text
The underlying model is trained specifically for Azerbaijani and handles the code-switching between Azerbaijani and Russian that is common in real contact centre calls — something generic engines routinely fail at.
Automated Quality Scoring
A hybrid scoring engine applies rule-based criteria, semantic AI understanding, and optional human override so QA results are both consistent and contextually accurate.
Complaint & Risk Detection
Negative sentiment, explicit complaints, and compliance risk phrases are detected across every call, surfacing issues that random sampling would miss entirely.
Single-Tenant Private Cloud
Your call recordings and transcripts are processed and stored in a dedicated environment. No data is shared with other tenants and no audio leaves your infrastructure boundary.
How Allmaz analyses your calls
Common questions about Allmaz speech analytics
Does Allmaz really analyse every call, or is there still a sampling layer involved?
Allmaz analyses 100% of calls with no sampling layer at any stage. Every conversation is transcribed, diarised by speaker, scored by the hybrid QA engine, and checked for complaints and compliance risk signals. The full call population is what drives your reporting, not a statistical subset.
How does the platform handle calls where agents switch between Azerbaijani and Russian mid-conversation?
The speech-to-text model is purpose-built for exactly this scenario. It is trained on the mixed AZ/RU speech patterns that are common in Azerbaijani contact centres, so code-switching within a single call is handled correctly and does not degrade transcription accuracy the way a generic, English-first engine would.
Where is our call data stored and processed, and does any of it leave our environment?
Everything is processed and stored inside a single-tenant private cloud that runs within your own dedicated infrastructure boundary. No recordings, transcripts, or derived data egress to shared infrastructure, and there are no cross-border data transfers to foreign vendor systems. This gives your compliance and data-governance teams a clean, auditable data residency position.
Can our QA team still review and override automated scores?
Yes, and this is a core part of the design. The hybrid scoring model explicitly supports human override at the individual call level. Supervisors can adjust any automated score, add context, and those corrections feed back into the scoring process to improve future results over time.
We currently use a different speech analytics platform. How complex is the migration to Allmaz?
The Allmaz team works with you through the full onboarding process. Because the platform is deployed inside your own environment, integration with your existing telephony infrastructure and CRM systems is scoped and planned as part of the implementation engagement rather than left to your team to figure out independently.
See what 100% call coverage looks like for your contact centre
Request a demo and let the Allmaz team show you how purpose-built Azerbaijani speech analytics works in practice — inside your own private cloud.
Request a demo