Alerts you'll act on.
Never alerts you'll mute.
Data-change intelligence for Snowflake and Databricks. AI-native, restraint-positive, transparent in what it costs and how it thinks. We watch your warehouse for the silent stuff — and only tell you when it matters.
Average yearly cost of poor data quality, per organization.
GartnerOf data engineers' time spent firefighting silent failures.
Wakefield ResearchOf data quality issues are caught first by downstream business users — not by the data team.
Monte Carlo · state of data qualityYour existing tools cry wolf. We don't.
A carbon monoxide detector for your data warehouse.
Most data quality tools are smoke alarms. They fire constantly, train you to ignore them, and tell you what changed without telling you whether it matters. We do the opposite. We watch for the silent stuff: a column that started returning nulls, a sync that stopped completing, a table that quietly disappeared. Most days you don't hear from us. When you do, the alert is contextual, timely, and specific enough to act on.
We're statistics-only by design. We never read raw row contents. We synthesize what you've told us about your environment with what we observe in your warehouse, then we explain — in plain language, with our reasoning shown — what changed and whether it matters. The product runs above whatever native detection your platform already provides. It's the intelligence layer on top, not another smoke alarm.
Here's what an alert looks like.
Generated by our cascade against a production warehouse, formatted for the people who get paged at 3 a.m.
The column crm.customers.phone was dropped between the 10:00 and 11:00 UTC snapshots, observed as a schema-snapshot diff at 11:00:30 UTC. No DDL audit event has been captured upstream.
crm.customers is annotated business_critical and customer_facing with a 1-hour SLA. Three registered consumers depend on this table: revenue_dashboards, customer_facing_app, and email_marketing_campaigns. The customer_facing_app performs real-time reads and is presumed broken immediately; email_marketing_campaigns runs as a batch pipeline and will fail on its next execution.
The drop occurred after the weekday BI morning refresh window (09:00–10:00 Helsinki), which completed without incident, placing the DDL operation in the 10:00–11:00 UTC window. No corroborating deploy or migration event has been observed. Schema-dropped-column on a business_critical, customer_facing table with three active consumers constitutes an immediate breakage risk rather than a degradation risk.
SELECT user_name, query_text, query_start
FROM snowflake.account_usage.query_history
WHERE query_text ILIKE '%DROP COLUMN%phone%'
AND query_text ILIKE '%crm.customers%'
AND query_start > '2026-04-29 09:00'
ORDER BY query_start DESC LIMIT 10;
git log --since='2026-04-28 00:00' --all --grep='phone' \
-- models/ migrations/
SELECT error_message, COUNT(*) AS occurrences
FROM app_error_log
WHERE error_message ILIKE '%phone%'
AND occurred_at > '2026-04-29 10:00'
GROUP BY error_message
ORDER BY MAX(occurred_at) DESC LIMIT 10;
Walk through the dashboard.
One screen, 100 tables, real-time health at a glance. The demo shows the workspace as a Snowflake customer with hourly, daily, and on-demand checks would see it — incidents surfaced, spend tracked, no clicking required.
Four things incumbents can't copy without rebuilding.
We tell you why.
Native platform DQ surfaces anomalies. We explain them — with full warehouse context, lineage, deploy history, and your own annotations folded into the reasoning.
Every alert is auditable.
Three confidence numbers visible on every alert. A full decision trace per incident. We tell you why we believe what we believe — and exactly what we're unsure about.
Cross-vendor verification.
Different LLM providers generate and verify each alert. Different failure modes catch different errors. We trust no single model with the final word.
Cost throttles depth, not outcome.
You set spend caps per dimension. The system stays watching, just thinks less hard about ambiguous cases when budget is tight. Detection never stops.
Pay for what you use. Cap what you spend.
One product. One rate card. No tiers, no feature gates, no seat-based pricing. Your bill is a function of three things you can measure and predict — and you can cap any one of them independently.
Each snapshot of each monitored table. The cheap, high-volume layer — the foundation of trust.
When a signal needs evaluating, we run cheap-model composition and reasoning. We charge for every one — and show every one on your invoice, including those we dismissed.
When something is high-stakes or genuinely ambiguous: full agentic loop, premium models, cross-vendor verification, transparent cost. These are rare.
$50 free at signup
Every account starts with $50 of usage credits. No card required. Most teams take several months to consume it.
$20 monthly minimum
Active accounts pay at least $20/month. Most teams clear that bar in their first week. Doesn't apply during your $50 trial — credits come first.
Customer-controlled caps
Set monthly caps on any dimension. Approach a cap, get warned. Hit a cap, the system gracefully reduces depth — but never stops watching.
Optional annual commit
Lock in a higher monthly minimum and get 15% off your per-check rates. Quick and deep analysis stay flat — those costs pass through honestly.
Stop muting your alerts.
$50 in free credits. No card. Connect your Snowflake or Databricks in fifteen minutes. Tell us what matters; we'll watch the rest.