platform

An agent that notices when your data drifts.

Broken pipelines rarely announce themselves — a schema shifts, a dedupe step silently fails, two systems drift apart. Your agent profiles across stores on a schedule and tells you what changed, in plain language.

Why it works here

Data quality monitoring, the Contextually way.

Cross-store consistency

The check that matters most — does the warehouse still agree with the source system — requires reaching both. Single-store DQ tools can't ask it.

Checks written per table

Volume, nulls, distributions, referential drift — the agent writes checks suited to each table's shape instead of a generic threshold sweep.

Findings, not dashboards

What returns is a short brief: what drifted, since when, likely blast radius. The kind of alert someone actually reads.

The flow of data
postgres.orders (2% sample) ~160,000 rows snowflake.orders_mirror ~160,000 rows contextually runtime profile + drift checks read-only · audited 2 material findings 486 tokens → context
The program, case by case
goal → nightly drift check: warehouse vs source systems, key tables
src = profile(sql("postgres", "SELECT * FROM orders"), sample=0.02) wh = profile(sql("snowflake", "SELECT * FROM orders_mirror"), sample=0.02) diff = drift(src, wh, checks=["volume","nulls","distribution","keys"]) return brief(diff, only_if="material", notify="data-platform")
profiled 6 key tables across 3 stores returned 2 material drift findings · 486 tokens → context
Get started

See it on your data.

Book a demo and we'll run data quality monitoring against systems like yours — your stores, your policies, live — and map the path to a deployment in your VPC.