Call-center speech analytics for Government
Call-center speech analytics for government. Public bodies require on-premise systems so citizen data never leaves the country.
Speech Analytics Built for Government Call Centers
Public-sector contact centers carry a unique responsibility: every citizen interaction must be handled with accuracy, accountability, and strict data protection. Allmaz delivers an on-premise speech analytics platform that transcribes, diarises, and scores 100% of calls — not a sample — so government bodies can monitor service quality, detect compliance risk, and protect citizen data without a single byte leaving the country. Unlike sampling-based approaches that leave the majority of conversations unexamined, full-coverage analysis gives agency leadership and audit teams a complete, defensible record of every exchange between staff and citizens.
Why Government Agencies Choose Allmaz
Full data sovereignty: a single-tenant private cloud deployment ensures all processing and storage occur within your own infrastructure, so citizen data never crosses national borders or reaches any third-party environment.
Complete call coverage: every conversation is transcribed, diarised, and scored, eliminating the blind spots and systemic gaps that make sampling-based quality programs unreliable for public-sector accountability.
Faster identification of citizen complaints: automatic detection of negative sentiment and complaint patterns alerts supervisors in time to intervene before issues escalate into formal grievances or public complaints.
Audit-ready evidence trail: time-stamped, diarised transcripts and detailed score records are searchable and exportable, giving oversight bodies and procurement reviewers a clear, tamper-evident log of service delivery.
Native Azerbaijani and mixed AZ/RU accuracy: the speech-to-text engine is purpose-built for the language patterns common in citizen calls, maintaining transcription accuracy across Azerbaijani, Russian, and code-switched conversations without manual language selection.
Human-in-the-loop quality assurance: supervisors can review any automatically scored call and apply an override that is recorded alongside the automated score, keeping human judgment at the centre of public-service accountability.
Platform Capabilities
100% Call Transcription and Diarisation
Every call is automatically transcribed and separated by speaker, producing a clear, time-stamped record of what each party said — no manual note-taking, no gaps in coverage.
Complaint and Sentiment Detection
The platform flags calls containing complaints, negative sentiment, or elevated citizen frustration in real time, allowing quality teams to prioritise reviews and reduce public-service response times.
Compliance Risk Scoring
Automated checks identify conversations that may breach procedural or regulatory requirements, giving compliance officers an early warning system backed by a full call record.
Hybrid QA Scoring
Scores are generated through a combination of rule-based criteria, semantic AI analysis, and human override — balancing consistency with the contextual judgment that government quality standards demand.
Purpose-Built Azerbaijani Speech-to-Text
Trained specifically for Azerbaijani and mixed AZ/RU speech, the transcription engine handles the language patterns common in citizen calls without the accuracy degradation seen in generic multilingual models.
Single-Tenant Private Cloud
The entire platform runs within your own infrastructure boundary. There is no shared environment, no data egress, and no dependency on external cloud providers — meeting the strictest public-sector data-sovereignty requirements.
How It Works
Frequently Asked Questions
Does the platform send any citizen data to external servers?
No. The single-tenant private cloud architecture means all processing and storage occur entirely within your own infrastructure. There is no data egress to Allmaz servers or any third-party cloud environment at any stage of transcription, analysis, or storage.
Why does analysing 100% of calls matter for government agencies?
Sampling-based approaches leave the vast majority of interactions unexamined, which can conceal systemic service failures, recurring compliance breaches, or emerging patterns of citizen dissatisfaction. Analysing every call gives management, quality teams, and auditors a complete and defensible picture of service delivery — one that holds up to formal scrutiny.
How does the platform handle calls where agents switch between Azerbaijani and Russian mid-conversation?
The speech-to-text engine is purpose-built for this language environment and is specifically designed to handle mixed AZ/RU speech within a single call. It maintains transcription accuracy throughout code-switched conversations without requiring agents or supervisors to manually select a language at any point.
Can our quality team adjust or override the automated scores?
Yes. Human override is a core feature of the hybrid QA scoring model, not an afterthought. Supervisors can review any automatically scored call and apply their own judgment. That override is recorded alongside the original automated score, preserving a complete audit trail of both the system's assessment and the human decision.
How does the platform support procurement and audit transparency?
Every call produces a time-stamped, diarised transcript paired with a detailed score record that captures which rule-based criteria and semantic signals contributed to the result. These records are fully searchable and exportable, giving auditors, oversight bodies, and procurement reviewers a clear, structured log of citizen interactions and the quality assessments applied to them.
Ready to Bring Full Accountability to Your Citizen Contact Center?
Speak with the Allmaz team to see how our on-premise speech analytics platform can be deployed within your agency's infrastructure — protecting citizen data while giving you complete visibility over every call.
Request a demo