Blockchain Analytics
On-chain intelligence · design stage
A planned blockchain analytics platform. Data pipelines, APIs, dashboards and infrastructure lifecycle controls are in design; no analytics backend is live.
Explore the project
- Review the planned data-system boundaries and operating requirements.
- Distinguish the proposed capabilities from the lab’s working AI demos.
Problem and current status
The project is investigating how to turn blockchain transactions into inspectable analytical data while keeping expensive infrastructure controllable. It is currently design work, with no live backend or dashboard.
Planned data architecture
Future implementation will specify supported chains and sources, ingestion, indexing, transformations, storage, APIs and dashboards. Provider selection, data freshness and indexing strategy are unresolved. This is an information architecture for future work, not an implemented system diagram.
Analytics and evaluation
Transaction and wallet exploration are candidate use cases. Anomaly detection or ML will be described as functionality only once implemented and evaluated against real data.
Infrastructure and cost requirements
The future platform should use Infrastructure as Code and command-line lifecycle operations for deploy, status, scale-down or pause where supported, and destroy. Concrete commands will be documented when they exist. This site’s unapplied Lightsail configuration is separate from the analytics platform.
Security and operations plan
Environment isolation, monitoring, secrets management, predictable startup/shutdown, budget controls, protected infrastructure state and safe teardown of persistent data are requirements for the future build.
Limitations and open decisions
No data source, FastAPI service, Chainstack integration, The Graph index, ML model or compliance guarantee is implemented in this checkout. The project illustration is concept art, not a dashboard screenshot.
Learning and next work
Implementation lessons are pending. The next dedicated effort should define a small data slice, source provenance, reproducible infrastructure and acceptance checks before expanding analytics functionality.