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Work

Proof, in production.

Representative engagement patterns from the founder's enterprise practice — the shapes of work Refinity takes on.

Selected founder experiencePatterns, not client case studies. Details are generalized.

Semantic layer at scale

A BI estate rebuilt on a governed semantic layer

Migrating enterprise reporting onto dimensional models sized against real capacity limits — so the layer survives production query loads, not just the demo dataset.

What you receive

  • Governed dimensional models
  • Sized against real capacity limits
  • Holds up under production query loads

Agent-ready analytics

Giving AI agents governed access to the numbers

Structured, governed agent access to semantic models — with evaluation harnesses gating what agents are allowed to answer, so wrong answers get caught before decisions do.

What you receive

  • Governed agent access to semantic models
  • Evaluation harness gating every answer
  • Wrong answers caught before decisions

BI delivery pipeline

Quality gates on every model change

Git-integrated BI development with automated best-practice checks on every pull request — a short, fixed-scope engagement that upgrades how the whole team ships.

What you receive

  • Git-integrated BI development
  • Automated best-practice checks per pull request
  • Short, fixed-scope engagement

Initial acquisition focus · Broader semantic foundation

Is your Power BI model ready for Copilot—or only for dashboards?

Start with one consequential question. Refinity examines the model, definitions, AI context, security, and evaluation evidence needed to make the answer defensible.

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Make the next answer worth trusting.

Bring the question your team cannot answer consistently. Refinity will trace the definitions, data, and checks needed to make it dependable.

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