Real-time entity extraction for your Rasa bots
Swiftask empowers Rasa by extracting complex entities instantly, turning conversation flows into actionable data.
Result:
Boost NLU precision and accelerate request processing with augmented AI.
AI Agents
rasa
Connector rasa · Secure OAuth 2.0
Limitations of native entity extraction
While Rasa is powerful, extracting entities from unstructured or complex data can overwhelm your NLU models. Handling exceptions and dynamic entities often requires intensive development.
Main negative impacts:
Lack of contextual precision
Standard models struggle to identify specific entities in long or ambiguous conversational contexts.
Operational latency
Complex processing within Rasa can slow down bot responses, degrading the final user experience.
NLU model maintenance
Retraining your models for every new entity is expensive and time-consuming for technical teams.
Swiftask acts as an intelligence layer added to Rasa. It processes messages in real time to extract precise entities, allowing your dialogues to remain fluid and relevant.
BEFORE / AFTER
What changes with Swiftask
Standard Rasa workflow
The bot receives a user message. The NLU model attempts to extract entities. In case of failure or ambiguity, the bot fails to understand, asks for clarification, or triggers a fallback, frustrating the user.
Rasa + Swiftask approach
Swiftask intercepts the message in parallel. It extracts complex entities with precision. These enriched data points are sent to Rasa, enabling immediate and accurate responses without manual effort.
Deploying entity extraction in 4 phases
1
STEP 1 : Pipeline configuration
Connect your Rasa instance API to Swiftask to enable real-time data transfer.
2
STEP 2 : Entity schema definition
Configure the entity types that the Swiftask agent needs to identify in your conversations.
3
STEP 3 : Intelligent processing
Swiftask analyzes incoming text, extracts entities, and formats them for Rasa.
4
STEP 4 : Integration and execution
Rasa receives the processed entities and executes the corresponding actions without delay.
Swiftask analysis capabilities
Contextual identification, multi-entity extraction, data normalization, and cross-validation.
- Target connector: The agent performs the right actions in rasa based on event context.
- Automated actions: Real-time extraction, Rasa slot enrichment, multilingual support, and dynamic entity management.
- Native governance: The integration ensures near-zero latency while maintaining your data compliance.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-rasa@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.
AI benefits for your bots
Increased precision
Drastically improve entity recognition rates even in complex texts.
Reduced development
Less need for intensive custom NLU model training.
Conversational fluidity
Faster responses thanks to optimized external processing.
Business scalability
Add new entity types without redeploying the entire Rasa infrastructure.
Structured data
Automatically transform conversations into data ready for your CRM.
Security and privacy
Swiftask applies enterprise-grade security standards for your rasa automations.
- Secure processing: Data flows through encrypted channels between Rasa and Swiftask.
- Data isolation: No data is kept for training third-party models.
- Full control: You manage permissions and access at the finest granularity levels.
- GDPR compliance: Architecture designed to respect European data protection standards.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Performance indicators
| Metric | Before | After |
|---|---|---|
| Recognition rate | 75% (standard) | 98% (augmented AI) |
| Average latency | 500ms+ | < 100ms |
| Maintenance effort | High (retraining) | Low (configuration) |
| Fallback rate | 20% | Under 5% |
Take action with rasa
Boost NLU precision and accelerate request processing with augmented AI.