Sentiment analysis in calls
AI speech analytics that transcribes, scores quality, and detects complaints and risk across 100% of your calls — in your own private cloud.
What Is Sentiment Analysis in Calls?
Sentiment analysis in calls is the automated process of examining spoken conversations to identify the emotional tone, intent, and attitude expressed by each speaker. By combining speech-to-text transcription with natural language understanding, the technology classifies moments in a conversation as positive, neutral, or negative and flags signals such as frustration, explicit complaints, or compliance risk. Unlike manual review processes that depend on spot-checks and subjective judgment, automated sentiment analysis applies a consistent, objective lens to every interaction — ensuring that no conversation is evaluated differently based on who reviewed it or when.
Why Sentiment Analysis Matters for Your Contact Centre
Full call coverage with no blind spots: every conversation is transcribed, diarised, and scored, so no risky or non-compliant interaction can slip through an unmonitored gap that sampling would leave open.
Consistent, bias-free quality scoring: automated evaluation removes the inconsistency and personal bias inherent in manual spot-checks, producing objective assessments that hold up across teams and time periods.
Near-real-time complaint detection: negative sentiment patterns and explicit complaint language are surfaced as soon as a call is processed, enabling supervisors to prioritise resolution before issues escalate.
Proactive compliance risk management: systematic flagging of compliance-sensitive phrases helps your team stay ahead of regulatory obligations rather than discovering violations after the fact during audits.
Accurate results for Azerbaijani and mixed-language calls: a purpose-built speech recognition model handles the mixed AZ/RU conversations that are common in local contact centre operations, delivering transcription and scoring quality that reflects the real linguistic reality of your calls.
Complete data sovereignty: a single-tenant private cloud architecture ensures all voice recordings, transcripts, and derived insights remain entirely within your own infrastructure with no data egress to external environments.
Core Capabilities of Allmaz Call Sentiment Analysis
100% Call Coverage
Every conversation is transcribed, diarised by speaker, and scored — eliminating the blind spots that come with traditional sampling approaches.
Complaint and Risk Detection
The system automatically identifies negative sentiment patterns, explicit complaint language, and compliance-sensitive phrases, tagging them for immediate review.
Azerbaijani-First Speech-to-Text
A purpose-built model handles Azerbaijani and mixed AZ/RU speech accurately, reflecting the real linguistic reality of calls in the region.
Hybrid QA Scoring
Quality scores combine rule-based criteria, semantic AI evaluation, and human override — giving supervisors a structured yet flexible framework for agent assessment.
Private Cloud Deployment
The platform runs in a single-tenant environment with no data egress, so sensitive call recordings and transcripts never leave your infrastructure.
Speaker Diarisation
Each call is automatically separated into agent and customer turns, making sentiment scores and quality metrics attributable to the right participant.
How Allmaz Analyses Sentiment Across Your Calls
Frequently Asked Questions
Does the system analyse every call or only a random sample?
It analyses 100% of calls without exception. Every conversation is transcribed, scored, and checked for complaints and compliance risk, so there are no unmonitored interactions regardless of call volume.
How accurately does the platform handle Azerbaijani and mixed-language calls?
Allmaz uses a purpose-built Azerbaijani speech-to-text model specifically designed to handle mixed AZ/RU speech, which is the everyday linguistic reality in many contact centres operating in Azerbaijan. This dedicated model avoids the accuracy gaps that arise when general-purpose engines encounter regional language patterns.
Where is our call data stored and processed?
All transcription, scoring, and analysis happens inside a single-tenant private cloud deployment assigned exclusively to your organisation. There is no data egress at any stage, meaning your recordings, transcripts, and derived insights remain entirely within your own infrastructure.
Can our quality assurance team still apply their own judgment to scores?
Yes. The hybrid QA scoring model layers rule-based checks and semantic AI evaluation with a human override capability. Supervisors retain full authority to review any flagged interaction, adjust scores, and confirm or escalate identified risks — the automation supports their judgment rather than replacing it.
What specific types of risk does the sentiment analysis detect and flag?
The system detects negative sentiment patterns, explicit complaint language, and compliance-sensitive phrases within conversations. Each of these signal types is tagged and surfaced for timely review, allowing your team to prioritise the interactions that carry the greatest operational or regulatory risk.
Ready to Understand Every Customer Conversation?
Allmaz brings full-coverage sentiment analysis, complaint detection, and hybrid QA scoring to your contact centre — built for Azerbaijani speech and deployed entirely within your private cloud. Get in touch to see how it works for your team.
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