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Practice 01 · Data AI/ML

Turn years of data into a moat.

A governed data platform, and models fitted to your business, with every answer traced to data you trust.

See the Vividly story
Why now

“Finance, risk and the data team each bring a different number to the board, and now we want AI on top of it.”

One wordCustomer

  • Finance

    An account we billed this quarter

  • Risk

    A legal entity we carry exposure to

  • Data team

    A unique ID in the CRM

Three definitions, three numbers. Put AI on top, and it repeats the disagreement.

How the semantic layer fixes it
Capabilities

From raw data to trusted answers.

Six disciplines, from the first data decision to the evidence your risk team reads.

  1. Data strategy

    Put data to work on the lines you report.

  2. Data platforms and governance

    Make every number traceable.

    Read Governance by design

  3. Data confidence and AI readiness

    Know your data is fit before AI depends on it.

  4. Semantic layer

    Give people, reports and agents one language.

    See the semantic layer

  5. Machine learning and model fine-tuning

    Fit the model to your business, and prove it.

  6. AI evaluation and assurance

    Show the evidence before anyone asks.

    Knowing the limits
Under the hood

Data agents can trust.

Trust in AI-powered analytics starts with the data platform underneath it.

Can we trust agents?

People trust dashboards because the data under them went through engineering rigor and a governance strategy. Agents are a different animal: give them the same foundation, and the semantic layer on top.

  1. Agents
  2. Dashboards
  3. Semantic layer
  4. Data products
  5. Modeling
  6. Ingestion
  7. Sources

Governance

  • Stewardship
  • Data lineage
  • Metadata management
  • Access control
  • Data security
  • Data profiling
  • Data quality

Engineering practice

  • Version control
  • CI/CD
  • Observability
  • Automated testing
  • Orchestration
  • Infrastructure as code
  • Modularity and reuse
  • The rigor runs all the way up to the dashboards.
  • Agents read through the semantic layer, not around it.

The semantic layer: one source of truth

Business logic, metric definitions and data relationships are defined once by your data team. Every agent reads the same definitions.

Semantic layer

  • Assistant agents
  • BI agents
  • Data agents
  • Custom agents

Data access

  • Natural language to SQL
  • Extra burden of authored context for every question

Semantic accessRecommended

  • Natural language to the semantic model
  • Builds on the rigor already in your semantic model
How we work

Prove the data, then build.

Forward Deployed Strategists (FDS) find the data a use case needs and prove it is fit. Forward Deployed Engineers (FDE) build the platform and the models on it.

  1. Hands pointing at printed charts on a meeting table01 · FDS

    Forward Deployed Strategists (FDS)

    • Find the data one use case really needs
    • Score its quality and trace the gaps
    • Decide what to build first, on the evidence
  2. Two colleagues talking through a dashboard on a laptop02 · FDE

    Forward Deployed Engineers (FDE)

    • Build the governed platform and pipelines
    • Fit and evaluate the model against a baseline
    • Hand over the evidence pack and runbooks
See the full maturity ladder
Proof

When the data is trusted, people serve themselves.

All case studies
The data journey

One data journey, four stages.

Where your data sits today decides the first step.

Where the data work goes, stage by stage

  1. Build the foundation

    Assess the data

    Current data maturity, structure and model.

  2. Migrate to the cloud

    Move it and stream it

    Historical data migrated, real-time streaming set up.

  3. Modernize for scale

    Modernize the sources

    Authoritative sources modernized, with compatibility layers for legacy tools.

  4. Transform with AI

    Consolidate for AI

    Data consolidated across the enterprise into entity models.

Honest about AI

Know where AI answers belong.

An analyst checking a printed chart against the numbers on her laptop

Knowing the limits

Natural-language analytics is strong for some decisions and wrong for others. We say which, up front.

  • Suitable for directional decisions

    Identifying high-performing campaigns to prioritize, analyzing historical performance trends such as quarter-over-quarter comparisons, and understanding why.

  • Not suitable for critical decisions

    Avoid using it for precise financial reporting, such as SEC filings.

  • Evolving technology

    The technology is still under development and improving rapidly. It may generate responses that sound convincing but are factually incorrect, so validation is crucial: always verify the information provided.

Meet our leaders

The person who leads this work.

Get started

Put your data to work.

Talk to our data team.

Recommended first stepBook an AI Value Accelerator sessionYour leaders leave with a ranked heatmap, a business case kit and a roadmap.Something else in mind?Talk to our teamTell us what you need. A CWX lead replies personally.

Prefer email? sales@cwx.tech

Other practicesPractice 01 of 04
02 · Engines that power AI outcomes

Platform Engineering

03 · Move tech teams faster with generative AI

Transformation Office

04 · Put agents to work across the value chain

AI Engineering