Our implementation is built to handle any read volume and any low-latency requirement — from a public block explorer serving millions of requests per day to a dashboard refreshing every block, with the same underlying pipeline. That flexibility is what makes the same stack a fit for every product surface below.
Block Explorers
High-QPS lookups for blocks, transactions, addresses, and events at chain-tip latency.
Dashboards
Interactive analytics surfaces that refresh in seconds, not minutes — public or internal.
Real-time APIs
Low-latency REST and GraphQL endpoints powering live feeds, notifications, and in-app data.
Cross-chain Analytics
Unified schemas across every ecosystem we index — query many chains with one shape.
Protocol Forensics
Replay history, reconstruct incidents, and audit on-chain activity against the source of truth.
…and anything else SQL can power.
Bring your workload, we’ll size the stack to it.
Any read volume, any latency target
The pipeline scales independently of the surface it feeds. Whether you need:
- Thousands of QPS behind a public-facing explorer or wallet,
- Sub-second freshness for a live trading or governance dashboard,
- Billion-row aggregations for ad-hoc research,
…the same Numia infrastructure is sized to it. We provision dedicated read capacity per workload, so heavy analytical queries don’t degrade your live API and vice versa.
Real-time product surfaces
The same stream that powers analytics also powers product. Common surfaces we serve include:
- Live wallet activity feeds powering CRM, notifications, or in-app inboxes.
- Real-time leaderboards for volume, fees paid, or governance participation.
- Alerting on event patterns — large transfers, validator slashing, bridge / IBC failures.
Cross-chain analytics
Every ecosystem we index follows a consistent shape, so fan-out queries across many chains stop being per-chain glue code and start being a single composable query. New chains plug into the same surface without rewriting your dashboards.
Protocol forensics & ad-hoc exploration
Because we retain the unmodified on-chain payload, you can always go back and re-derive new columns or replay history without re-indexing — making this a strong fit for:
- Incident investigation — reconstruct what happened during an outage, slashing, or exploit.
- Schema iteration — build new derived views from raw data without re-ingesting the chain.
- Indexer parity checks — diff your own indexer against the source-of-truth payload.