Case Study · Cross-industry
SmartComm Analyzer
Reviewing communications for themes, risk signals and follow-up actions.
- Industry
- Cross-industry
- Category
- Decision Support
- Capabilities
- Conversational Intelligence, Decision Support
The problem
Large volumes of customer and internal communications made it hard for managers to spot emerging issues and commitments.
Existing manual process
Managers read message queues and call notes to find issues and commitments, usually by sampling.
Agent / workflow deployed
An agent that classifies messages, tags sentiment and risk, extracts actions and produces a prioritised digest.
Systems touched
The workflow
What the agent does, step by step.
- 01
Message ingestion
- 02
Classification
- 03
Sentiment and risk tagging
- 04
Action extraction
- 05
Prioritised digest
Managers review flagged items and own responses.
Operating model
Before and after.
Before
Sampled review; issues found when customers escalated.
After
Every message classified and prioritised; managers act on a daily digest.
Measured results
Verified before published.
No metrics are published for this case yet. Once the customer confirms the measured change against their baseline (turnaround, cost per transaction, accuracy), we will add it here.
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