Data trust and enterprise AI

Make your enterprise data AI-ready.

We profile, validate and govern the data you already have, then build AI products and custom GenAI on top of it, so every answer can be traced back to data you trust.

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AWS and Snowflake implementation partner

app.zeusgenai.in / data-trust
Data Trust
Overview
SchemaPulse
Data Genome
DQ Check
DataGov
Workspace
Sources
Alerts
Checks passed today1,28499.2% pass rate
Datasets profiled48across 5 sources
Schema changes caught32 need review
Checks passed · last 14 days
Live activity
cust_id renamed in ORDERSSchemaPulse · 10:41
14 orders with negative amountDQ Check · 09:15
customers profiled, 24 columnsData Genome · 09:02
DatasetSourceLast runStatus
SALES_DB.ORDERSSnowflake2 min agoSchema change
SALES_DB.PAYMENTSSnowflake5 min agoHealthy
CRM.CUSTOMERSSQL Server12 min ago14 failed rows
Works withSnowflakeAWSDatabricksAzureOracleSQL ServerMongoDB

Most AI projects don't fail because of the model. They fail because a column was renamed, a number was never checked, or two teams define revenue differently.

Data Trust

Three products for data you can trust

SchemaPulse

Know when a schema changes, before your reports do

Source systems change without warning. SchemaPulse compares every run with the last and alerts the table owner immediately.

  • Watches tables across Snowflake, Databricks, Oracle, SQL Server and MongoDB
  • Shows exactly what was renamed, added or removed
  • Alerts the table owner and can hold downstream jobs
More about SchemaPulse →
SALES_DB.ORDERScompared with yesterday, 06:00
order_idNUMBER
−cust_idVARCHAR
+customer_idVARCHAR
+discount_codeVARCHAR
−legacy_flagBOOLEAN
amountNUMBER(12,2)
Owner notified · downstream refresh on hold06:02
Data Genome

Understand your data before anyone builds on it

Data Genome profiles each dataset column by column, so your team starts from facts instead of assumptions.

  • Data type, null rate, value range and distinct count for every column
  • Each run is compared with the previous one, so drift is easy to spot
  • Shareable profile reports for engineers, analysts and data owners
More about Data Genome →
CRM.CUSTOMERS24 columns · profiled 09:02
ColumnTypeEmptyDistinctShape
customer_idNUMBER0%31,560
signup_dateDATE0%1,204
cityVARCHAR0.4%212
emailVARCHAR2.1%30,890
email: empty values up from 0.3% last runreview
DQ Check

Write a rule once. It runs on every load.

Readable quality rules that run inside your warehouse on every load, with failing rows surfaced for remediation.

  • Declarative rules instead of hand-written SQL tests
  • Runs inside your warehouse. No data leaves your environment
  • Every failure is reported with the affected rows and the rule that failed
More about DQ Check →
checks.ymlrun 06:00 · SALES_DB
# illustrative orders: - not_null: order_id - min: column: amount value: 0 customers: - format: column: email pattern: email
✓order_id not null0 failed
✕amount ≥ 014 failed
✓email format0 failed
2 of 3 passed · 14 rows to review

Also in Data Trust: DataGov, the full data governance platform.

The rest of the suite

AI for documents and business operations

Built on the same data foundation. Adopt one product, then add more as your needs grow.

How it fits

One trusted layer between your data and every AI that uses it

We validate your data, give it one shared business meaning, and only then put AI on top of it. That is what makes the answers reliable.

SOURCESTRUSTMEANINGUSED BY Snowflake Databricks Oracle SQL Server MongoDB ProfileData Genome ValidateDQ Check MonitorSchemaPulse Ontology + USLUnified Semantic Layer CustomerOrder RevenueChurn One definition of every metric AI agents & assistants Zeus AI products Dashboards & reports

Don't get AI to your Data. Get your Data to AI.

OntologyUSL (Unified Semantic Layer)

How each layer works →

Platform

Built for teams that can't afford bad data

Designed for day-to-day production use, not just for the demo.

People stay in charge

Data owners approve changes before anything downstream is updated.

Interactive example
Schema change detectedSALES_DB.ORDERS · cust_id → customer_id

Works with your stack

Connects to the warehouses and databases you already run.

SnowflakeDatabricksOracleSQL ServerMongoDBAWSAzure

Your data stays in your environment

Checks run inside your warehouse. No data is copied out.

✓Snowflake · checks run in placeno export

Explore the platform →

What changes for your data team

WithoutWith Zeus AI
A source column is renamedA dashboard breaks and an engineer traces the cause by hand.SchemaPulse flags it on the next run and tells the table owner.
Bad rows reach the warehouseThey surface weeks later in a finance report.DQ Check catches them on load and identifies the affected rows.
Someone asks what a field meansThe answer depends on who you ask.Data Genome and the semantic layer give one shared definition.
An AI answer looks wrongNo one can tell if the model or the data is at fault.The underlying data is validated and defined, so every answer can be traced.
Getting started

From first conversation to production

An indicative timeline for a first product. We confirm the plan with you in the first meeting.

1
Week 1

Define the use case

Agree the priority problem and the data in scope.

2
Week 2

Connect and profile

Connect your sources and profile the data to set a baseline.

3
Week 3

Go live

Quality checks and schema alerts run automatically.

→
After

Scale

Add products or build custom GenAI on the same foundation.

How we work with you →

See it running on your own data

A live walkthrough of SchemaPulse, Data Genome and DQ Check, led by the engineers who built them.

Contact sales
Zeus AI

See it running on your own data

Share a few details about your data and priorities. Our team will contact you to schedule a session.

  • Led by the engineers who built the products
  • Focused on your priority use case
  • Shown on the platforms you already use
Prefer to speak with us directly?sales@zeusgenai.in+91 88974 22733LinkedIn · Instagram

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