Automate complex FAQs by pairing Rasa with your AI agents
Swiftask enhances your existing Rasa bots. Transform static knowledge bases into intelligent conversations without manually managing intents.
Result:
Increase customer relevance while drastically reducing model maintenance time.
AI Agents
rasa
Connector rasa · Secure OAuth 2.0
Manual Rasa FAQ maintenance limits your scalability
Maintaining a high-performing FAQ on Rasa requires constant effort. Adding a question, changing an answer, or managing linguistic variations often requires retraining the NLU model. The result: high technical debt and outdated answers.
Main negative impacts:
Slow update cycles
Any policy change requires a development and training cycle, slowing down customer support.
Complex intent management
The proliferation of intents to cover every user question makes the bot difficult to maintain and increases collision risk.
Rigid user experience
Bots relying solely on fixed patterns struggle to handle nuanced natural language or out-of-scope questions.
Swiftask acts as a generative intelligence layer on top of Rasa. Your bots retain their dialogue structure while delegating document search to Swiftask.
BEFORE / AFTER
What changes with Swiftask
Traditional Rasa approach
A user asks a unique question. The bot finds no matching intent and returns a 'I don't understand' message. The team must create a new intent, write examples, retrain the model, and deploy.
Rasa + Swiftask
The Rasa bot detects an FAQ request. It queries Swiftask, which analyzes your documents in real time to generate a precise, contextual answer. No model modification needed.
Deploy FAQ automation in 4 steps
1
STEP 1 : Index your knowledge in Swiftask
Upload your PDFs, URLs, or document bases to Swiftask. The AI agent becomes an expert on your content.
2
STEP 2 : Configure the Rasa connector
Use the Swiftask API to create a bridge between your Rasa custom actions and the semantic search engine.
3
STEP 3 : Implement the search action
In your Rasa project, add a simple action that sends the user question to Swiftask and retrieves the answer.
4
STEP 4 : Test and validate
Verify the relevance of generated answers in your pre-production environment and adjust the agent's tone if needed.
Capabilities of your hybrid FAQ agent
Deep semantic analysis of user context, identification of non-FAQ intentions, and strict adherence to brand tone.
- Target connector: The agent performs the right actions in rasa based on event context.
- Automated actions: Automatic answers based on document sources, intelligent reformulation, source citation, native multilingual support.
- Native governance: You retain full control over Rasa for transactional scenarios, Swiftask manages knowledge.
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.
Benefits of this hybrid architecture
Real-time updates
Update a document in Swiftask: your Rasa bot answers immediately with the new information.
Zero NLU retraining
No more training cycles to add text content. Your Rasa model remains stable.
Extended coverage
Answer thousands of questions without creating thousands of intents.
Contextual answers
Generative AI adapts the response to the user's specific tone and context.
High-performance hybridization
Keep control of business processes with Rasa and the agility of AI for knowledge.
Data security and compliance
Swiftask applies enterprise-grade security standards for your rasa automations.
- Data isolation: Your documents are isolated and used exclusively to answer your users.
- Source control: You precisely define which documents serve as the basis for answers.
- Full audit trail: Track every FAQ interaction in Swiftask logs to improve quality.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Impact on bot performance
| Metric | Before | After |
|---|---|---|
| Update delay | Days (training) | Seconds (upload) |
| Resolution rate | Low (intent-limited) | High (broad coverage) |
| Technical maintenance | High | Minimal |
Take action with rasa
Increase customer relevance while drastically reducing model maintenance time.