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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

AI finance dashboard studio.

A source-flexible, HTML-based dashboard experience that refines data and uses AI to propose useful executive views, commentary, and user-requested edits.

HTML dashboardsData viewsAI orchestrationExecutive reporting
Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Finance users need different views of the same data, but rebuilding dashboards for every audience is slow and often disconnects commentary from the source view.

Decision frame

Questions the work needed to answer.

  • Which visualization is appropriate for this decision?
  • What should an executive see first?
  • Can a user ask a question or request an edit without rebuilding the dashboard?
Illustrative operating flowNo internal data, configuration, or client detail.
Approved sourceExisting viewData refinement

Operating layer

AI visualization loopDecision support with review and ownership.
Executive viewCommentaryAsk + edit

Implementation

From the business problem to a working operating model.

Built an HTML-based dashboard workflow that can consume approved sources or views, refine the dataset, recommend visual treatments, and support natural-language questions and changes.

  1. 01

    Accepted data from approved sources and existing views.

  2. 02

    Created a refinement loop to improve data readiness before presentation.

  3. 03

    Used an AI-assisted visualization loop to select an appropriate story, chart treatment, and executive commentary.

  4. 04

    Added an ask-and-edit layer so users can request analysis or adjust the presentation in plain language.

Controls, approvals, and delivery constraints

The work around the work.

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

  • The visual recommendation is reviewable and should not override finance judgment.
  • Source selection, transformations, and user edits need traceable context.
  • The public case study uses illustrative examples only, never internal finance data.

Public-safe outcome

A reusable way to move from approved data to a decision-focused dashboard without treating every reporting request as a fresh build.