Analyze sentiment in Botmaker with AI agents
Swiftask connects your AI agents to Botmaker to analyze the tone of every customer interaction in real time.
Result:
Instantly identify unhappy customers and prioritize critical interventions.
AI Agents
botmaker
Connector botmaker · Secure OAuth 2.0
Support teams struggle with uncontrolled conversation volumes
In Botmaker, messages flood in without emotional priority. An angry customer waits in the same queue as a simple inquiry, degrading customer experience and overloading your staff.
Main negative impacts:
Inappropriate response times
Emotional emergencies get lost in the noise, delaying resolution for critical cases.
Team burnout
Agents process tickets linearly without visibility into the customer's emotional context.
Declining customer satisfaction
Failure to detect negative sentiment prevents proactive crisis management.
Swiftask integrates sentiment analysis directly into your Botmaker workflows to classify, alert, and route conversations based on urgency.
BEFORE / AFTER
What changes with Swiftask
Without Swiftask
Your agents process Botmaker messages in order of arrival. A very dissatisfied customer waits for hours, increasing frustration and risking a PR crisis.
With Swiftask + Botmaker
AI analyzes every incoming message. If the sentiment is negative, the ticket is immediately flagged as priority and assigned to a supervisor.
Deploy sentiment analysis in 4 steps
1
STEP 1 : Create your analysis agent in Swiftask
Configure your agent to monitor Botmaker streams with sentiment analysis logic.
2
STEP 2 : Link Botmaker to Swiftask
Connect your accounts to allow the AI to access messages in real time.
3
STEP 3 : Define alert thresholds
Set actions to trigger based on sentiment scores (e.g., Slack alert for very negative sentiment).
4
STEP 4 : Supervise interactions
View sentiment scores in your Swiftask dashboard and adjust your responses.
Advanced features for your conversations
AI evaluates vocabulary, syntax, and emotional context to assign a reliable sentiment score.
- Target connector: The agent performs the right actions in botmaker based on event context.
- Automated actions: Automatically tag conversations by sentiment. Alert managers in real time. Prioritize tickets in the CRM. Generate weekly reports on customer morale.
- Native governance: All data is processed to comply with your privacy standards.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-botmaker@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.
Why adopt Swiftask for Botmaker
Proactive crisis management
Detect unhappy customers before they become detractors.
Resource optimization
Your best agents focus only on high-emotional-value cases.
Actionable data
Measure the evolution of customer sentiment over time with precise metrics.
No-code workflow
Integrate analysis without a single line of code or data expertise.
NPS improvement
Adapted and fast responses mechanically increase your satisfaction score.
Security and compliance
Swiftask applies enterprise-grade security standards for your botmaker automations.
- Data encryption: Your Botmaker conversations are processed securely.
- Privacy guaranteed: No customer data is used to train public models.
- Full audit trail: Total traceability of analyses performed on every message.
- Access control: Fine-grained management of access rights to your AI agents.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Measurable impact on your support
| Metric | Before | After |
|---|---|---|
| Emergency handling time | Several hours | Under 5 minutes |
| Customer satisfaction (CSAT) | Average | Significant increase |
| Agent productivity | Linear processing | Intelligent prioritization |
Take action with botmaker
Instantly identify unhappy customers and prioritize critical interventions.