AI Agent

Contact Center Analytics

Batch transcription and analytics over contact-center calls

The challenge

Thousands of calls are recorded and almost none are listened to. Quality assurance samples one or two percent, compliance breaches surface weeks later if at all, and the reasons customers keep calling back stay locked inside audio that nobody has time to open.

The outcome

Azure AI Speech transcribes every call with speaker separation, Azure AI Content Safety redacts personal data before any analysis, and Azure OpenAI scores topic, sentiment and compliance. Supervisors review flagged calls instead of queues, and every score links back to the exact transcript segment behind it.

At a glance

Type: speech ai

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

Recordings, chat logs and IVR events land in Blob and are picked up on a schedule. Speech transcribes and separates the speakers, Content Safety redacts personal data before any analysis, and the Foundry agent scores every conversation against the quality and compliance rubric — publishing metrics, searchable transcripts and supervisor alerts.

Figure 1 — End-to-end reference architecture for contact-centre conversation analytics on the Microsoft stack.

02 — Workflow

Process & decision workflow

How a conversation is collected, transcribed, redacted, analysed and scored — then branched. Healthy calls roll up into the trend dashboards, while a compliance breach or a low quality score raises a supervisor review with the transcript and evidence attached.

Figure 2 — Collect → transcribe → redact → analyse → score → risk branch → dashboard or review.

03 — Components

Key Microsoft components

Conversation analytics needs accurate transcription, careful redaction and a scoring model a supervisor can defend — all on the Microsoft stack.

Azure Communication ServicesVoice, SMS and chat channels with call recording.
Azure AI SpeechBatch transcription, diarisation and language identification.
Azure AI Content SafetyPersonal-data redaction and unsafe-content screening.
Microsoft Foundry Agent ServiceScoring pipeline, checklists and evaluation tools.
Azure OpenAI modelsTopic, intent, sentiment and summarisation over transcripts.
Azure AI SearchHybrid and vector search across every transcript.
Azure Logic AppsBatch scheduling, alerting and coaching-task creation.
Azure FunctionsChunking, normalisation and metric calculation.
Azure Blob StorageRecordings, transcripts and redacted artefacts.
Azure SQLConversation records, scorecards and reporting tables.
Azure Cosmos DBPer-call metrics, state and evaluation history.
Azure Key VaultKeys, retention secrets and access control.
Azure Container AppsScalable transcription and analysis workers.
Power BI / Microsoft FabricQuality, compliance and customer-experience dashboards.

04 — AI

What the agent consumes

The capabilities applied to every conversation, and the line the analytics do not cross.

AI capabilities embedded in the agent

Batch speech to text
Speaker diarisation
Language identification
Personal-data redaction
Topic modelling
Intent detection
Sentiment and emotion
Compliance checklist scoring
Call summarisation
Anomaly detection
Semantic search
Workflow orchestration

AI responsibility boundaries

Analytics inform coaching and compliance; they do not make employment decisions on their own. Personal data is redacted before analysis, retention follows the agreed policy, and every score links back to the exact transcript segment so a supervisor can confirm or overturn it.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the channels, the analysis template, the scorecards and the value model — the page structure stays identical.

Channel & scope profile

Define the channels in scope, languages, consent and retention rules, and whether coverage is a sample or every conversation. The personas are the agent, the supervisor, the quality analyst and the compliance owner.

06 — Impact

Key outcomes & business impact

Starting targets for the value case — validate each one against the customer baseline during discovery.

Quality coverage100%Every call scored instead of a small manual sample.
Review effort−70%Supervisors review flagged calls rather than entire queues.
Insight latencyOvernightCalls are analysed and on the dashboard the following morning.
Compliance evidencePer callEach score links to the exact transcript segment behind it.