Skip to content

Products AI-Native Data Trust & Control Plane

04 · TRUSTCan we trust this data?

Karsient Veriq

From Data Quality to Data Trust.

Veriq sits across your existing data platforms and continuously understands, protects, governs, and improves data as it moves — so problems are contained and explained before they reach the business, not discovered after.

karsient-veriq — command centerDATA TRUST SCORE91.4Active Incidents6Quarantined312Auto-Resolved84%Avg. Resolution11mTRUSTPASSWARNQUARANTINEREMEDIATEREVALIDATERELEASESMART QUARANTINE — ROOT CAUSE GROUPEDcustomers.date_of_birth — format anomaly1,204 recordsHigh impactorders.customer_id — referential integrity88 recordsMedium impactgold.revenue_kpi — aggregation drift3 datasetsHigh impactsilver.shipments — duplicate keys412 recordsLow impact

Why it exists

Problems Veriq solves

Bad data travels through the platform undetected until it hits a dashboard or a decision

A single bad record can stop an entire pipeline instead of just the records that need attention

Thousands of failures get investigated one by one instead of as a single root cause

Rule engines require constant manual upkeep as data and sources evolve

Executives have no single, trustworthy view of enterprise data health

How it works

Inside the Veriq process

Detect → Govern → Quarantine → Resolve → Trust

01

Detect

Continuously evaluate data as it moves — at record, batch, dataset, and pipeline level.

02

Quarantine

Contain suspicious or defective records as investigable incidents instead of silent failures.

03

Investigate

Veriq Copilot explains what happened, why, where it originated, and what it affects.

04

Remediate

Apply deterministic fixes automatically, or route governed fixes for steward approval.

05

Revalidate & Release

Repaired records pass back through quality controls before re-entering the trusted layer.

Under the hood

Inside the Data Trust Layer

Veriq doesn't wait for a nightly job to fail. It evaluates data continuously as it moves through your platform, using a decision framework that treats every record on its own merits rather than pass/failing an entire batch.

1

Every record is scored against layer-aware expectations — Bronze checks ingestion integrity and schema drift, Silver checks standardization and referential integrity, Gold checks business rules and KPI accuracy

2

The Trust → Pass → Warn → Quarantine → Remediate → Revalidate → Release framework decides the outcome per record, not per batch

3

When failures spike, the Quality Intelligence Graph traces them back to a single upstream cause — a schema change or source defect — instead of surfacing thousands of unrelated alerts

4

Veriq Copilot turns that root-cause analysis into a plain-language answer a data steward can act on without paging an engineer

Capabilities

What's inside

01

Veriq Connect

Universal, configuration-driven connectors for Databricks, Snowflake, Azure, AWS, Spark, Kafka, dbt, and orchestration platforms — attach without a pipeline redesign.

02

Veriq Observe

Continuous profiling, monitoring, and anomaly detection that adapts its expectations to Bronze, Silver, and Gold layers automatically.

03

Veriq Govern

Rules, policies, ownership, lineage, and a full audit trail — governance by design, not bolted on afterward.

04

Veriq Quarantine

Record-level containment that turns every failure into an investigable incident, not a silent rejection.

05

Veriq Copilot

A conversational AI data-steward assistant that explains failures with evidence and root cause, not just an alert.

06

Veriq Resolve

Deterministic fixes execute automatically within policy; governed fixes route to a human for approval.

07

Veriq Replay

Repaired records are revalidated through the same quality controls before re-entering the trusted layer.

08

Veriq Command Center

An executive data-intelligence view of enterprise Data Trust Score, incidents, and resolution trends.

09

AI root-cause intelligence

Correlates thousands of failures back to a single upstream schema change or source defect.

10

Quality Intelligence Graph

Connects source, pipeline, dataset, column, rule, failure, and remediation into one queryable graph.

11

Adaptive quality intelligence

Proposes candidate quality expectations from historical data and past incidents — AI proposes, governance approves.

12

Data Trust Score

A multi-dimensional score across accuracy, completeness, consistency, freshness, and business impact, drillable to record level.

Integrations

Works with what you already run

Databricks
Snowflake
Microsoft Fabric
Microsoft Azure
AWS
Google Cloud
Apache Spark
Apache Kafka
SQL
dbt
Apache Airflow

See it on your data

Get a walkthrough of Karsient Veriq

We'll run it against a sample of your own schema or code so you can see exactly what it surfaces.

FAQ

Common questions

No — Veriq is a plug-and-play quality layer. A data engineer attaches it to an existing pipeline through configuration and starts receiving quality intelligence immediately.

The toolkit

The rest of the Karsient product suite

Karsient is a product company too

Use Karsient Veriq standalone, or as part of a Karsient-led modernization engagement