Every engagement starts with understanding. Then we build.
DataOps is the data quality and pipeline health foundation every AI initiative depends on — most organizations build AI before they build the foundation that makes it reliable. DataOps changes that.
DataOps Foundation is a structured engagement that begins by understanding your data environment, your pipelines, and your business — then implements continuous monitoring, establishes baselines,
and builds the processes your team needs to own data quality long-term.
Request a Free Data Strategy Review
Why Data Quality Can’t Be a One-Time Project
New pipelines break. Business logic changes. Teams grow. Data quality degrades continuously — which means the tooling, processes, and ownership that keep it healthy need to be part of how your organization works — built right, or built better.
Annual cost of poor data quality
to the U.S. economy*
Lost per year by 1 in 4 organizations due to bad data
Of COOs say data quality
is their top AI risk
How To Get Started
Data Health Review
We understand your data stack, pipelines, and quality risks then propose a prioritized Foundation plan.
DataOps Foundation
Structured implementation. We build it, you own it. Full handoff with runbook and maturity score.
Choose Your Path
Self-operate, co-manage with dbSeer, or full managed program. You decide what fits your team.
3-Tier Monitoring Model
Seconds – Every pipeline trigger
Structural Health
Row counts, null rates, schema changes — fast checks that can gate pipelines before bad data flows downstream.
Can block a pipeline on critical findings
Minutes – Post pipeline async
Statistical Health
Full distributions, percentiles, column drift — catches subtle degradation invisible to row count checks.
Updates baselines after each run
Scheduled – Business cadence
Business Health
Named metrics sliced by dimension — revenue by region, orders by channel — where real business signal lives.
Where data meets business outcomes
What The Foundation Delivers
Built on your infrastructure – Owned by your team
Monitoring stack implementation
DataOps toolkit deployed on your existing stack — Databricks, Snowflake, Redshift, or your platform of choice.
Ownership & Alert Routing
Data ownership mapped, findings routed to the right team automatically
Business Engagement Process
Cadence and templates for expert review and quarterly stakeholder alignment
Baseline & Anormaly Detection
Self-learning baselines across your priority data assets — no manual rules required
Runbook & Team Enablement
Your team trained and equipped to operate, tune, and expand independently
Maturity Score & Roadmap
Where you are, where you’re going, and what it takes to reach the next level
After Foundation
Self-Operate
Co-Manage
Full Managed
DataOps Maturity Journey
Visible
Monitoring deployed — you can see what’s happening across your entire data stack
Reliable
Detection tuned, ownership clear — your data does what it’s supposed to, consistently
Intelligent
Processes mature, quality improving continuously — your data environment gets smarter over time
Integrations
Cloud / Warehouse
AWS Glue
Redshift
S3
Snowflake
BigQuery
Lakehouse
Databricks
Deltalake
Unity Catalog
Alerting
Slack
Webhook
PagerDuty
Semantic Layer
DBT
MetricFlow
OSI
Unity Catalog