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Experience atOmnicom Group

Driving financial systems and automation across global teams.

Builder ofEnterprise AI Systems

AI agents, audit workflows, and decision intelligence at scale.

Based inNew York City

Building systems for enterprises around the world.

© 2026 · New York City
All enterprise work

Enterprise implementation case study

Governed analytics and natural-language access.

A permissioned analysis layer that lets finance users ask questions over approved views and inspect supporting context.

DatabricksUnity CatalogAI/BI
Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Finance teams want faster answers, but unrestricted AI access can create an unreliable or uncontrolled route into sensitive data.

Decision frame

Questions the work needed to answer.

  • What can this user ask and see?
  • Which approved view supports the answer?
  • How can a user inspect and challenge the result?
Illustrative operating flowNo internal data, configuration, or client detail.
Approved data domainUser permissionsCurated views

Operating layer

Governed analysis layerDecision support with review and ownership.
Natural-language questionSupporting contextReviewable answer

Implementation

From the business problem to a working operating model.

Connected approved planning data to a governed analytics layer so authorized users can ask questions, inspect results, and view supporting context.

  1. 01

    Selected approved data domains and consumption views.

  2. 02

    Applied user access boundaries before the natural-language layer.

  3. 03

    Designed answer paths that keep source and result context available for review.

Controls, approvals, and delivery constraints

The work around the work.

Enterprise systems change only when data, access, testing, ownership, and evidence move together.

  • Natural-language output is decision support, not an unverified system of record.
  • User permissions and source context stay visible.
  • No prompt, model, or internal-data implementation detail is disclosed publicly.

Public-safe outcome

Plain-language analysis with clear data access boundaries and no fabricated answers.