Problem

Velum Labs addresses a common but costly data-quality failure: different teams can query the same business metric and get different answers even though their dashboards appear healthy and their queries return no errors. The resulting investigation—tracing pipelines, transformations, joins, filters, and team-specific logic—can consume days of data-engineering time, while multiplying across every metric and dashboard. The core economic pain is lost engineering capacity, executive distrust, and the risk that decisions are made from inconsistent numbers.

Its clearest use case is resolving metric divergence, such as finance reporting one revenue figure while product analytics reports another. Velum is particularly aimed at data teams in regulated financial institutions, where inaccurate or inconsistent data can cost money and trust and where the company says its system is already being used in production.

Product / Service

Velum positions itself as an operating system or semantic control plane for data quality. It continuously monitors query patterns, value distributions, and schema changes; builds a live dependency graph from production query traffic; and traces a discrepancy to the precise source of divergence, such as a different data source, filter, scope, join, transformation, or metric definition. It then proposes a unified definition, generates the required migration, and deploys the fix through existing Git, dbt, and CI/CD workflows.

The product closes the loop by turning each resolved incident into an enforceable data contract, without requiring teams to author and maintain YAML rules manually. It is designed to connect to existing warehouses and data tools—including Snowflake, BigQuery, Databricks, Redshift, Postgres, Kafka, Fivetran, and Airbyte—so the benefit is continuous detection and prevention without replacing the customer’s data stack.

Market

Velum competes in data quality, data observability, data lineage, and data-engineering infrastructure, with a differentiated emphasis on semantic consistency and automated remediation rather than only anomaly or schema detection. Adjacent alternatives include Great Expectations and Soda for validation, dbt tests for manually authored assertions, and Monte Carlo for statistical data monitoring. The company’s claimed distinction is learning metric meaning from query behavior, tracing semantic divergence, and generating deployable fixes and contracts.

The company is an early-stage B2B software startup founded in 2025, part of Y Combinator’s Winter 2026 batch, with a two-person team based in San Francisco. Its website reports production use at regulated financial institutions, more than 200 monitored tables, and more than $1 billion in assets under management across two countries. It is still operating an early-access and design-partner program, offered free during early access; no public revenue figure is disclosed, so it is best characterized as early commercial traction rather than a scaled revenue business.