Index Alchemy data automatically using AI
Swiftask links your AI agents to Alchemy data streams. Transform raw transactions into structured, actionable insights instantly.
Result:
Save valuable time on on-chain analysis and accelerate decision-making based on reliable data.
AI Agents
alchemy
Connector alchemy · Secure OAuth 2.0
The complexity of manual on-chain data indexing
Extracting and indexing data from Alchemy often requires significant technical resources. Manual processing of blockchain logs and events is slow, error-prone, and expensive in development time.
Main negative impacts:
Data access latency
The delay between an on-chain event and its exploitation reduces your ability to react quickly to market movements.
High technical complexity
Maintaining indexing pipelines requires dedicated engineers, diverting your teams from their core business.
Unstructured data
Raw Alchemy data is difficult to interpret without a robust indexing and normalization process.
Swiftask automates indexing via AI agents that filter, structure, and store your Alchemy data according to your specific needs, without writing a single line of code.
BEFORE / AFTER
What changes with Swiftask
Traditional approach
Developing custom scripts to query Alchemy, managing indexing servers, and constant maintenance to adapt to protocol updates.
The Swiftask advantage
Visual configuration of your AI agents. Data is indexed and processed automatically upon receipt, ready to be used by your business tools.
4-step indexing process
1
STEP 1 : Agent initialization
Define your AI agent in Swiftask with a clear mission for specific data indexing.
2
STEP 2 : Alchemy integration
Use your Alchemy credentials to allow your agent to access real-time data streams.
3
STEP 3 : Rule configuration
Configure filters and desired output formats for your indexed data.
4
STEP 4 : Deployment and monitoring
Activate the flow and monitor indexing directly from your Swiftask dashboard.
Data processing capabilities
The agent analyzes transactions, smart contract logs, and specific events defined in Alchemy.
- Target connector: The agent performs the right actions in alchemy based on event context.
- Automated actions: Automatic metadata extraction, format normalization, intelligent event filtering, and delivery to your databases or BI tools.
- Native governance: Swiftask ensures full traceability of every indexed data point to guarantee the reliability of your analyses.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-alchemy@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.
Major operational benefits
Speed gains
Drastic reduction in the time between on-chain occurrence and data availability.
No-code accessibility
Allows business analysts to configure indexing without depending on developers.
Native scalability
Swiftask infrastructure handles traffic spikes without your intervention.
Ready-to-use data
Data arrives structured and ready for your analytical models or reports.
Cost reduction
Less time spent on development and server maintenance.
Data security and integrity
Swiftask applies enterprise-grade security standards for your alchemy automations.
- Encrypted connection: All calls to Alchemy are secured via HTTPS and API authentication.
- Data governance: You maintain full control over indexed data and its destination.
- Compliance: Full history of indexing actions for internal audits.
- Isolation: Each agent instance is isolated to ensure the confidentiality of your sources.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Automation performance
| Metric | Before | After |
|---|---|---|
| Indexing time | Hours (manual/script) | Real-time |
| Maintenance cost | High | Minimal |
| Reliability | Variable | High (AI logs) |
| Setup time | Days/Weeks | Minutes |
Take action with alchemy
Save valuable time on on-chain analysis and accelerate decision-making based on reliable data.