Detect complaints in calls
AI speech analytics that transcribes, scores quality, and detects complaints and risk across 100% of your calls — in your own private cloud.
Detect complaints in every call — automatically
Most contact centers review only a small fraction of their call volume through manual sampling, which means complaints, compliance risks, and negative sentiment routinely go undetected in the conversations no one listens to. Allmaz speech analytics changes that by transcribing, diarising, and scoring every single call inside a single-tenant private cloud — giving your quality assurance team complete visibility across 100% of customer interactions rather than a statistically uncertain slice of them. Because no sampling is involved, no complaint is structurally invisible, and no compliance risk can hide in the unreviewed majority.
Why teams choose Allmaz for call complaint detection
Complete call coverage — every conversation is analysed without exception, eliminating the blind spots that sampling-based review leaves behind
Automatic complaint and negative-sentiment flagging reduces the manual effort required to find at-risk calls and lets your team focus on resolution rather than discovery
Compliance risk is surfaced proactively across all calls, giving supervisors the opportunity to intervene before an issue escalates into a formal complaint or regulatory event
Purpose-built Azerbaijani speech recognition accurately handles naturally occurring mixed Azerbaijani and Russian speech, reflecting how your agents and customers actually communicate
All call data — audio, transcripts, scores, and metadata — is processed and stored exclusively in a single-tenant private cloud dedicated to your organisation, with no data egress to external services
A hybrid QA scoring model combining rule-based logic, semantic AI understanding, and human override capability keeps your quality standards precise, auditable, and fully under your team's control
What the platform does
100% call transcription and diarisation
Every call is automatically transcribed and split by speaker, giving you a complete, searchable record of every customer interaction without manual effort.
Complaint and negative-sentiment detection
The system identifies complaint language and negative sentiment across all calls, flagging conversations that need attention so nothing slips through.
Compliance risk scoring
Each call is evaluated for compliance risk using a hybrid approach that combines rule-based checks, semantic AI analysis, and the ability for human reviewers to override scores.
Purpose-built Azerbaijani speech-to-text
The speech recognition engine is built for Azerbaijani and handles naturally occurring mixed Azerbaijani and Russian speech, reflecting how your agents and customers actually talk.
Single-tenant private cloud deployment
Your call data is processed and stored in a dedicated environment. No audio, transcripts, or metadata are shared with or routed through external services.
Hybrid QA scoring
Quality scores are generated by combining deterministic rules, semantic AI understanding, and human override capability, so your QA standards are always reflected accurately.
How complaint detection works, step by step
Common questions about Allmaz call complaint detection
Does the system analyse every call or only a sample?
It analyses 100% of calls without exception. Because no sampling is used, complaints and compliance risks present in any conversation are captured — not only those that happen to be selected during a manual review cycle. This eliminates the structural blind spot that affects sampling-based quality assurance programmes.
How does the platform handle Azerbaijani and Russian mixed speech?
The speech-to-text engine is purpose-built for Azerbaijani and is specifically designed to handle conversations where agents or customers naturally switch between Azerbaijani and Russian mid-sentence or mid-call. This reflects the actual communication patterns found in Azerbaijani contact centers rather than assuming monolingual speech.
Where is our call data stored and processed?
Everything runs in a single-tenant private cloud environment dedicated exclusively to your organisation. No audio recordings, transcripts, quality scores, or any other derived data are sent to external services, shared infrastructure, or third-party systems at any point in the process.
Can our QA team adjust or override the AI-generated scores?
Yes. The hybrid scoring model is explicitly designed to incorporate human overrides. Your QA analysts can correct or refine any score, and those adjustments are reflected back into the system so that the model continues to align with your organisation's evolving quality standards.
What types of compliance risk does the scoring identify?
The scoring layer combines rule-based checks — such as verification that required disclosures were made or that prohibited phrases were not used — with semantic AI analysis capable of detecting risk in context, even when exact trigger words are absent. This dual approach reduces both false negatives from rigid keyword matching and false positives from context-free pattern detection.
Ready to find the complaints you are currently missing?
Talk to the Allmaz team to see how 100% call coverage and private-cloud deployment can work for your contact center.
Request a demo