OmniSight Web3 Data Engine — Real-time financial risk intelligence
I design and build end-to-end systems that turn raw blockchain activity into actionable financial insight. OmniSight ingests Base Mainnet USDC flows, decodes payloads, and scores wallet behavior with ML — production-grade, latency-aware, and API-first.
Why OmniSight matters
Web3 moves fast and ledger data is noisy. OmniSight turns raw USDC transfers into structured, ML-ready intelligence for compliance, fraud detection, and institutional reporting.
Core capabilities
- Autonomous ingestion (Airflow) with 120s cadence
- Hex decoding and normalization via web3.py
- Partitioned PostgreSQL fact tables for scale
- FastAPI gateway exposing metrics and ML scores
OmniSight Web3 Data Engine at a glance
A production-grade intelligence engine that ingests Base Mainnet USDC flows, decodes them into partitioned fact tables, and scores wallet behavior with an Isolation Forest model exposed via API.
System snapshot
- 120s ingestion cadence via Airflow pulling JSON-RPC logs
- web3.py decoder → typed analytics fields
- Partitioned tables (e.g., usdc_transfers_era_47m)
- FastAPI + Nginx for real-time streaming and API access
- Isolation Forest for anomaly detection and risk tiers
Business outcomes
- Compliance-grade monitoring for exchanges and institutions
- Real-time alerts for high-value movements and suspicious wallets
- Operational metrics for ingestion latency and partition health
Why it matters
Billions of dollars flow through USDC daily. OmniSight surfaces the signals that matter — large transfers, counterparty patterns, and anomalous wallet behavior — so teams can act quickly and with confidence.
Public API
OmniSight exposes documented endpoints for whale alerts and wallet risk prediction. The same APIs power dashboards, alerts, and integrations.
GET /api/v1/metrics/whale-alerts
GET /api/v1/predict/wallet-risk?wallet_address=0x...
From ledger to risk score in one coherent pipeline
OmniSight is designed for scale and observability: ingestion, decoding, partitioned storage, ML scoring, and API delivery.
Architecture diagram (conceptual)
Design choices: partitioning for scale, ML-ready schema, API-first delivery, and cloud-native deployment on OCI Linux.
Key design decisions
- Partitioned tables: fast queries at scale (example: usdc_transfers_era_47m)
- ML-ready schema: sender, receiver, amount, block, timestamp
- API-first: FastAPI gateway exposes metrics and predictions
- Observability: ingestion latency, block coverage, model health
Operational notes
- Airflow DAGs run every 120s; idempotent ingestion
- Partition maintenance supports high ingest rates
- Model retraining scheduled as needed; scoring is real-time
A dashboard built for decision-makers
The dashboard surfaces economic activity, risk signals, and operational health — all driven by live APIs.
Key performance indicators
| Block | Tx | Sender | Receiver | Amount (USD) | Ingested |
|---|---|---|---|---|---|
| Loading whale alerts… | |||||
Teams that benefit from OmniSight
I partner with Web3 infrastructure teams, fintech and compliance groups, exchanges, and analytics platforms to deliver production-grade telemetry and ML-driven risk intelligence.
Exchanges & Custodians
Real-time monitoring for large movements, counterparty tracing, and compliance workflows.
Compliance & AML
Automated alerts, risk tiers, and evidence-ready transaction trails for investigations.
Fintech & Analytics
Operational dashboards, institutional reporting, and data products built on reliable pipelines.
Let’s build something that matters
If you need architecture, data engineering, or ML for Web3 risk and compliance, reach out. I consult, partner, and build production systems end-to-end.
Engagements I take
- Proof-of-concept to production pipelines
- ML risk scoring and model operationalization
- API design and integration for analytics teams
- Architecture reviews and performance tuning