Pricing

Streamline Hasura updates with AI assistance

Swiftask integrates with Hasura to automate your data management. Benefit from intelligent, secure, and controlled update workflows.

Result:

Minimize human error and accelerate your data modification cycles.

The challenges of manual Hasura updates

Modifying data directly via GraphQL is risky. Syntax or logic errors can corrupt your information. Manual management hinders your technical team's velocity.

Main negative impacts:

Data error risks: An improperly formulated update can invalidate critical records, impacting your application's performance.
Technical bottlenecks: Developers waste significant time validating and executing repetitive update queries.
Lack of visibility: Tracing manually performed modifications is difficult, complicating audit and debugging efforts.

Swiftask acts as an intelligence layer on top of Hasura. Your AI agents prepare, validate, and execute updates according to your business rules, ensuring security and consistency.

BEFORE / AFTER

What changes with Swiftask

Traditional management

Developers must manually write GraphQL mutations, verify integrity constraints, and risk errors during every execution in the Hasura console.

AI-assisted updates with Swiftask

The AI agent receives an instruction, generates the secure mutation, verifies business conditions, and executes the update via the Hasura API securely.

Deploy your assisted updates in 4 steps

STEP 1 : Define schemas in Swiftask

Connect your Hasura instance and define the data models authorized for assisted updates.

STEP 2 : Configure validation agents

Set up the agent to verify business rules before any GraphQL mutation execution.

STEP 3 : Integrate your workflows

Trigger updates via events, webhooks, or natural language user interactions.

STEP 4 : Monitor and audit

Review the complete history of modifications performed by the AI in your Swiftask dashboard.

AI assistance capabilities for Hasura

The AI analyzes the GraphQL schema and database constraints to generate compliant mutations.

Target connector: The agent performs the right actions in hasura based on event context.
Automated actions: Secure mutation execution, logical validation before writes, detailed modification logs, context-based automation.
Native governance: Swiftask ensures every update is traceable and reversible if needed.

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.

Value for your operations

1. Data integrity

The AI validates data before any write, preventing database corruption.

2. Increased productivity

Automate repetitive updates and free up time for your developers.

3. Full governance

Precisely control who and what can be modified via AI.

4. Enhanced security

Reduce direct database access by using an intelligent abstraction layer.

5. Operational simplicity

Interact with your data using natural language or through automated processes.

Security and compliance

Swiftask applies enterprise-grade security standards for your hasura automations.

Secure authentication: Connection via encrypted Hasura API keys with granular permission management.
Mutation validation: Systematic verification of data compliance before execution.
Audit logs: Complete history of all actions performed on the database.
Access control: Role management and restricted access within the Swiftask platform.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Impact on your processes

Metric	Before	After
Update time	Minutes per query	Seconds
Data errors	Frequent	Near zero
Traceability	Limited	Full and automatic
IT dependency	High	Reduced

Take action with hasura

Minimize human error and accelerate your data modification cycles.

Supercharge your Hasura data with intelligent semantic search

Next use case