Are Your AI Initiatives Stuck?

DataOS Makes Enterprise Data AI-Ready

When analysts can’t trust, explain, or reuse data, AI use cases stall. DataOS fixes the data foundation so AI and analytics move to production.

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Works on top of your existing data stack

Learn more about DataOS

Why our customers trust us

What Changes When Analysts Get AI-Ready Data
  • First AI-ready use case in 4–8 weeks
  • Faster demand forecasting and scenario analysis
  • Cleaner dealer and network analytics
  • Fewer reconciliation cycles across teams
  • AI outputs that business leaders actually trust
  • Reduce total cost of data ownership by 50%
What customers achieve with DataOS

"In just eight weeks with DataOS, we had a data infrastructure in place that allowed us to transform how we use data."

Global Head of Technology, Lenovo
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What Analysts and Business Teams Are Running Into

Across manufacturing, retail, aviation, fintech, and healthcare labs, we hear the same things :

  • Analysts can’t get usable datasets without tickets and delays
  • The same metric means different things across teams
  • Forecasts, dealer reports, and dashboards don’t line up- AI experiments work in silos, then fail in production
  • No one can clearly explain where a number came from

The issue isn’t lack of tools.  It’s that data lacks business meaning, trust, and readiness for AI.

Talk to Us About Usable Data

Why This Blocks AI, Forecasting, and
Decision Speed

Why the best analysts are unable to make AI systems work

  • Data has no shared semantics
  • Lineage is incomplete or invisible
  • Quality checks are manual or inconsistent
  • Governance slows access instead of enabling it

When analysts don’t trust the data, AI outputs don’t get used by the business.

Talk to Us About Semantic Layer

What “AI-Ready Data” Means for Analysts and Business Teams

AI-ready data is not another dashboard or model.

  • Clear business definitions, not raw tables
  • Reusable, governed datasets they can trust
  • Lineage and quality built in, not documented later
  • Access without 20-day delays

When data is AI-ready, analysts stop fixing data and start driving outcomes.

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How Enterprises Fix This Without Rebuilding Their Stack

You Keep

  • ▪   Snowflake or Databricks
  • ▪   Existing BI and analytics tools
  • ▪   Current source systems

DataOS Adds

  • ▪   A semantic layer that standardizes business meaning
  • ▪   Data products analysts can reuse across AI and BI
  • ▪   Inline lineage, quality, and access control
  • ▪   Native access for dashboards, APIs, and AI apps

No rip-and-replace. No new silos. No disruption to existing teams.

Who DataOS is Built For

Global CPG Distributor

Heads of Digital Transformation and Business Analytics

Industrial Manufacturer

AI and Innovation
leaders accountable for outcomes

Global Architectural Firm

CIO, CTO, CDO balancing speed with trust

Leading Device Manufacturer

Enterprises where analysts drive decisions, not just reports

Start With a Real Business Use Case.
Don’t start with just a demo.

A real forecasting,
analytics, or AI 
use case

Implementation in
your environment

A working outcome
your analysts
can validate

A clear path
to production

Learn more about DataOS

DataOS is recognized by the experts.
Proven by results.

Talk to us about making your data usable for AI

Start with a discovery conversation that leads to a Proof of Value.
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FAQs

Is this another data platform we need to replace our current stack with?

No. DataOS sits on top of your existing systems like Snowflake or Databricks and works with your current BI and analytics tools.

How is this different from improving our data pipelines or models?

Pipelines move data and models use it. DataOS focuses on making data usable by adding business meaning, trust, and readiness before AI or analytics consume it.

Will this slow analysts down with more governance and approvals?

No. Governance is built into the data itself, so analysts get faster access without waiting weeks for tickets or manual checks.

How long before we see something working in our environment?

Most enterprises see their first AI-ready use case working in 4–8 weeks.

Can analysts actually use this, or is it only for data engineering teams?

It’s built so analysts and business teams can reuse trusted data products without depending on engineering for every request.

Does this only apply to AI use cases?

No. It also improves demand forecasting, dealer and network analytics, dashboards, and any decision-critical reporting.

What happens if our data definitions already differ across teams?

That’s expected. DataOS helps standardize definitions through a shared semantic layer instead of forcing teams to manually reconcile metrics.

Is this suitable for experimentation, or only large enterprise programs?

It works for focused use cases first and scales as more teams reuse the same trusted data foundation.