Interaction intelligence
Analyze calls, chats, and emails to identify complaint drivers, hidden dissatisfaction, failure demand, sentiment, and vulnerability signals.
- Calls
- Chats
- Emails
- Failure demand
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Complaint Intelligence
See the complaints your current process misses
Reported complaints are only part of the picture. ICOM and CXR use AI-assisted review to uncover hidden dissatisfaction, failure demand, vulnerable-customer signals, service breakdowns, and compliance exposure inside everyday interactions.
- Review 50,000 calls, chats, or emails as an initial evidence base.
- Use AI to classify, triage, and support complaint resolution workflows.
- Deliver first insight within 10 days, then scale toward every interaction.
Three Phases
Start with interaction review, then improve complaint operations
CXR can begin with an evidence review and expand into AI-supported complaint operations.
AI-powered complaint handling
Support complaint intake, classification, vulnerability detection, compliance assessment, and resolution recommendations while keeping human reviewers in control.
- Classification
- Vulnerability detection
- Compliance checks
Complaints operations transformation
Build an operating model that combines AI assistance, human oversight, governance controls, and leadership reporting.
- Operating model
- Human oversight
- Governance
Platform Context
Built around contact-centre AI and conversation analytics
CXR.AI brings contact-centre AI, real-time triggers, compliance checking, sentiment analysis, vulnerability indicators, and trend analysis into service review.
Conversation analytics
CXR draws on CXReview-style capabilities across transcription, redaction, sentiment analysis, vulnerability indicators, trend analysis, and customised checks.
- Transcription
- Redaction
- Sentiment
- Trends
Workflow and compliance triggers
Use in-call analysis and workflow triggers to surface advisory notices, compliance issues, serious alerts, and recommended actions faster.
- In-call analysis
- Workflow triggers
- Alerts
Case study evidence
IBM's case study explains how CXR moved beyond small sampled reviews toward AI-supported analysis of every interaction using IBM Watson technology.
- IBM Watson
- watsonx evolution
- Quality analysis
Business Case
Start with evidence, then decide what to change
The challenge
Most organisations see reported complaints but miss hidden dissatisfaction, recurring service issues, and early compliance risks inside everyday interactions.
- Hidden dissatisfaction
- Repeat causes
- Compliance exposure
The solution
AI reviews customer interactions, classifies complaint risk, detects vulnerable customers, and gives reviewers stronger evidence.
- AI triage
- Reviewer support
- Auditability
The starting point
Start with 50,000 customer interactions and identify value within 10 days through complaint drivers, failure demand, compliance signals, and improvement priorities.
- 50,000 interactions
- 10 days
- Improvement priorities
Outcomes
Use complaint data to decide what to fix
CXR connects customer interactions to decisions across complaints, quality, compliance, training, and operations.
Find the root causes behind complaint volume
Move beyond counting complaints and identify the process gaps, repeated friction points, agent behaviours, and service issues creating avoidable demand.
- Root causes
- Failure demand
- Service gaps
Support fairer and faster resolution
Give reviewers better signals, suggested next actions, and clearer evidence so complaint handling becomes more consistent and auditable.
- Reviewer support
- Next actions
- Audit trail
Turn customer insight into operational change
Use themes, trends, vulnerability signals, and compliance findings to prioritise training, process fixes, and leadership decisions.
- Trends
- Training priorities
- Leadership reporting
Questions
Complaint operations with oversight
CXR improves visibility, consistency, and resolution speed while keeping human governance in the process.
Is CXR only for formal complaints?
No. CXR can identify reported complaints and hidden dissatisfaction across customer calls, chats, and emails.
Does AI make the final decision?
No. AI supports classification, checks, recommendations, and insight. Human reviewers remain in control of resolution decisions.
Where does the IBM relationship fit?
The CXR material references IBM Watson-powered complaint resolution and the CXReview case study. ICOM uses that context for AI-supported service improvement and complaint operations work.
Next Step
Want to understand what your complaints are really telling you?
Start with an interaction review to identify complaint drivers, compliance signals, and fixes worth prioritising.