Supercharge your Hasura data with intelligent semantic search
Swiftask turns your Hasura databases into powerful semantic search engines. Query your data in natural language and get precise results.
Result:
Enhance user experience and search relevance without complicating your GraphQL infrastructure.
AI Agents
hasura
Connector hasura · Secure OAuth 2.0
Limitations of classic text search on Hasura
Traditional keyword search in Hasura often fails due to user intent mismatch. If the exact term isn't there, the result is empty. For complex databases, this limits engagement and data discovery.
Main negative impacts:
Irrelevant results
Text search ignores context and synonyms, frustrating users with imprecise matches.
Development complexity
Implementing advanced search on Hasura often requires complex and expensive indexing layers.
Degraded user experience
Search that doesn't understand natural language hampers SaaS adoption.
Swiftask interfaces with your Hasura data to inject a semantic search layer. Thanks to embeddings, your queries understand meaning, not just words.
BEFORE / AFTER
What changes with Swiftask
Classic Hasura search
A user searches for 'connection issues'. If the word 'connection' isn't in the database, no results are returned, even if articles on 'authentication' exist.
Swiftask semantic search
The AI agent understands 'connection issues' is a synonym for 'authentication'. It returns relevant results instantly via an optimized query.
Implement Hasura semantic search in 4 steps
1
STEP 1 : Connect Hasura to Swiftask
Configure access to your Hasura GraphQL endpoint securely within Swiftask.
2
STEP 2 : Index your data
Select the tables and fields for semantic indexing. Swiftask generates vectors automatically.
3
STEP 3 : Configure the search agent
Define response rules and filters so the AI agent queries your data with precision.
4
STEP 4 : Deploy the search API
Use the Swiftask API to integrate this smart search into your frontend or existing application.
Advanced search capabilities for Hasura
The agent analyzes the semantics of the user query to map concepts to data stored in your Hasura tables.
- Target connector: The agent performs the right actions in hasura based on event context.
- Automated actions: Vector search on your data, dynamic filtering via metadata, natural language support, seamless integration with your GraphQL schema.
- Native governance: Swiftask ensures search respects your security and data visibility rules configured in Hasura.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-hasura@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 choose Swiftask for Hasura
Increased precision
Semantic search understands the intent behind the user's query.
Rapid development
Add AI capabilities in minutes without rewriting your backend.
Optimized costs
Avoid heavy infrastructure to manage dedicated vector search.
Native security
Leverages your existing Hasura permissions to guarantee data access.
Scalability
Solution designed to grow with your data.
Security and compliance
Swiftask applies enterprise-grade security standards for your hasura automations.
- Secure authentication: Uses JWT tokens and Hasura roles to secure access.
- Data isolation: Swiftask only stores necessary vectors; raw data stays on your instance.
- Full audit: Traceability of all requests performed via the agent.
- Compliance: Adheres to B2B data protection standards.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Impact on your performance
| Metric | Before | After |
|---|---|---|
| Result relevance | Low (strict keyword dependence) | High (context awareness) |
| Implementation time | Weeks (custom development) | A few hours |
| User engagement | High bounce rate | Increased session duration |
Take action with hasura
Enhance user experience and search relevance without complicating your GraphQL infrastructure.