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Solutions · Data Foundation

Data the business understands. AI it can trust.

One shared meaning for your data, with governance that lives in the platform.

Every team reads the same numbers, and every AI system can show where its answers came from.

Talk to us about your data See the results
Client results

Data the business can use, under the controls it needs.

Each result below comes from the client's published case study.

Figures as published in each client's case study.

Why it matters

Every team has a number. None of them match.

AI inherits the arguments your data already has.

When revenue means three different things in three different dashboards, every model built on top inherits the argument. When the rules for sensitive data live in a document and in people's calendars, every new AI project waits for a meeting.

We've seen the fix sit in the platform, not in more process. Classify the data once. Define the metrics once. Enforce both automatically, and keep the evidence as a by-product of the work.

  • Conflicting numbers
  • Access by favor
  • Evidence assembled by hand
  • AI projects waiting on approvals
What you're hearing

The words that come before this work.

  • Our data foundation isn't ready for AI.

    Leaders want AI on data they do not yet trust.

  • Which number is right?

    Teams spend meetings reconciling instead of deciding.

  • Getting access means a chain of emails.

    Approvals depend on who you know.

  • Audit wants evidence we do not have in one place.

    Proof is assembled by hand, after the fact.

What we build

Classification, certified metrics and purpose-based access.

  • Compliance by design

    1. 01Classify

      Find and label sensitive data automatically.

    2. 02Tag

      Carry the label with the data everywhere it goes.

    3. 03Enforce

      Masking, access and retention applied by policy, not by hand.

    4. 04Evidence

      Every decision logged and mapped to the control it satisfies.

    Policies are code. Host sensitive data, don't hide it.

  • Metrics as contracts

    Certified metrics are defined once, owned by a named person and checked at every pull request. A change that conflicts with a certified metric fails before it merges.

    FAIL  governance / metric-conflict
  models/apac/daily_demand.sql defines a measure named demand
  A certified definition already exists: commercial.demand
  Owner: Commercial Finance
  > Reference the certified metric, or request a regional variant.

    What a new analyst sees on their first pull request.

    Fail early, and fail in CI.

  • Access follows purpose

    Access is granted to a data initiative with a stated purpose, approved by the data owner, provisioned automatically and removed when the work ends.

    1. Request
    2. Approve
    3. Provision
    4. Work
    5. Expire

  1. 01Discover and classify
  2. 02Codify
  3. 03Enforce and operate

The same three phases on every estate, run with your data owners.

Control

Governance that lives in the platform.

  1. 01

    Mechanisms over good intentions

    If a rule matters, the platform enforces it.

  2. 02

    Fail early, and fail in CI

    The cheapest place to catch a conflicting definition or a policy breach is the pull request.

  3. 03

    Policies are code

    Versioned, reviewed and tested like any other change.

  4. 04

    Host sensitive data, don't hide it

    Locking personal data in a corner nobody can reach isn't compliance.

  5. 05

    Developer experience is the control surface

    The platform owner decides the degrees of freedom.

Want a read on your estate first? The AI X-Ray maps your data, systems and controls.

Who's in the room

Your people and ours, one outcome.

Daniel JoanesSr. Director Data and AI

Leads Data and AI/ML.

Data AI/ML
Questions

Frequently asked questions

Something else on your mind? Ask us directly

  • No. Governance is applied where the data lives.

Get started

Give every team the same numbers.

Tell us the decisions your data has to serve and where trust breaks today. We will show you the first domain to fix.

Start the conversationTalk to us about your dataGo furtherRead the Vividly story

Prefer email? sales@cwx.tech