Analyze the sentiment of your chat interactions in real time
Swiftask turns your conversation volumes into actionable data. Your AI agents identify the emotions and intentions behind every message.
Result:
Gain clarity on customer satisfaction and react proactively to weak signals.
AI Agents
chat data
Connector chat data · Secure OAuth 2.0
High conversation volumes hide critical signals
Your support or sales teams handle thousands of messages. It is impossible to manually analyze every interaction to measure satisfaction or detect emerging frustration.
Main negative impacts:
Undetected churn risk
Customer dissatisfaction accumulating in chat history often leads to churn due to lack of early warning.
Underutilized unstructured data
The emotional richness of your exchanges is lost. You miss market trends or product needs expressed by your customers.
Inconsistent quality tracking
Without standardized analysis, quality assessment depends on the subjectivity of individual supervisors.
Swiftask automates sentiment analysis across all your chat data. The AI categorizes, scores, and alerts your teams to conversations requiring immediate attention.
BEFORE / AFTER
What changes with Swiftask
Without Swiftask
Managers manually sample a few conversations per week. Overall sentiment is based on partial impressions. Critical issues are identified too late, often after a formal complaint.
With Swiftask + Chat Data
Every message is analyzed instantly. The dashboard displays real-time customer sentiment trends. An alert is automatically triggered as soon as a conversation shifts to negative sentiment.
Setting up AI analysis in 4 steps
1
STEP 1 : Connect your data sources
Integrate Swiftask with your chat platforms (Zendesk, Intercom, Slack, etc.) to centralize message flows.
2
STEP 2 : Configure the analysis model
Set up the AI agent to identify emotional nuances specific to your industry.
3
STEP 3 : Define alert thresholds
Determine the sentiment levels that trigger a priority notification for your managers.
4
STEP 4 : Visualize the insights
Leverage automated reports to identify friction points and improve your processes.
Advanced analysis features
The AI evaluates polarity (positive/negative/neutral), emotional intensity, and the urgency of each interaction.
- Target connector: The agent performs the right actions in chat data based on event context.
- Automated actions: Automated satisfaction score. Frustration keyword detection. Intent categorization. Real-time alerts for critical cases. Trend reporting over time.
- Native governance: Analyses are correlated with customer data for a 360° view of the user experience.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-chat-data@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Value for customer experience
Reduced churn rate
Identify dissatisfied customers before they cancel their contracts.
Continuous product improvement
Transform customer feedback into a product roadmap using semantic analysis.
Augmented supervision
Your managers focus on high-value conversations.
Quality standardization
Apply consistent evaluation criteria to 100% of your interactions.
Strategic reactivity
Adapt your sales pitch based on the real emotions of your prospects.
Data privacy and ethics
Swiftask applies enterprise-grade security standards for your chat data automations.
- Data anonymization: Sensitive data is processed securely and anonymized before analysis.
- GDPR compliance: Swiftask adheres to the strictest standards for data protection.
- Environment isolation: Your analysis data is not used to train public third-party models.
- Controlled access: Granularly manage access rights to analysis reports within your organization.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Key performance indicators
| Metric | Before | After |
|---|---|---|
| Analysis coverage | 5% (manual sampling) | 100% (real-time AI) |
| Risk detection | Reactive (post-complaint) | Proactive (during exchange) |
| Analysis time | Several hours per week | Automatic / Instant |
| Insight accuracy | Subjective (human bias) | Objective (data-driven) |
Take action with chat data
Gain clarity on customer satisfaction and react proactively to weak signals.