- contact@verticalserve.com
The data and AI engineering side of web3 — on-chain and off-chain pipelines, real-time indexing and streaming, an analytics lakehouse for chain data, and governed AI agents that read it, built for protocol, infrastructure and digital-asset teams.
The same six problems show up for protocol, infrastructure and digital-asset teams.
Raw blocks, logs and traces are built for consensus, not analytics. Every team ends up writing its own decoders and indexers.
Market data, user activity, CRM and support live off-chain, so a full picture of a protocol or customer means joining two worlds by hand.
Reorgs, liquidations, outages and anomalies happen in seconds. Nightly jobs explain them the next morning.
Data quality, freshness and completeness need to be measured continuously, because downstream contracts and dashboards depend on them.
Agents that read chain data or draft actions need identity, policy and an audit trail before anyone lets them near production.
Risk, AML and regulatory reporting need lineage from the report back to the transaction, not a spreadsheet export.
Engineering for the pipelines, platforms and agents that sit between the chain and the people who use the data.
Ingest blocks, logs, traces and contract events alongside market, product and business data, decoded once into documented schemas.
Kafka and Flink pipelines that index chain activity as it lands, handle reorgs, and serve fresh state to apps and alerts.
Governed tables for transactions, contracts, wallets and protocol metrics on Databricks or open table formats, ready for BI and ML.
Usage, growth, retention and economic metrics per contract and network, defined once and served to every dashboard.
Freshness, completeness and anomaly checks on every pipeline and feed, with alerting that separates noise from incidents.
Agents that read chain and business data through governed tools, with on-behalf-of identity, policy outside the model and full traces.
Entity resolution, transaction lineage and case data for risk and compliance teams, with an audit trail from report to transaction.
Telemetry and usage analytics for SDKs, APIs and developer portals, so platform teams see adoption and friction.
How on-chain and off-chain data flow through one governed platform.
Data and AI engineering for a leading blockchain infrastructure company.
Writing from our team on blockchain, digital assets and the data underneath them.