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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

Real estate decision intelligence.

A governed real-estate portfolio app and Genie-based analysis surface for moving from scattered asset detail to a decision-ready portfolio view.

Databricks AppsDatabricks GenieReactNext.js
Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Real-estate teams needed one place to understand portfolio and asset detail without manually reconciling information across multiple sources and views.

Decision frame

Questions the work needed to answer.

  • Which properties or portfolio segments need attention?
  • What detail supports a portfolio-level conclusion?
  • How can users ask follow-up questions without losing the governed source context?
Illustrative operating flowNo internal data, configuration, or client detail.
Portfolio dataAsset detailException views

Operating layer

Governed portfolio app + GenieDecision support with review and ownership.
Portfolio viewAsset contextQuestion with source context

Implementation

From the business problem to a working operating model.

Designed a React and Next.js-based Databricks App for portfolio exploration, alongside a Genie experience for guided natural-language questions over approved real-estate data.

  1. 01

    Organized approved real-estate detail into portfolio, asset, and exception views.

  2. 02

    Built a Databricks-hosted application for a master portfolio experience instead of a static reporting page.

  3. 03

    Added a governed Genie path for natural-language analysis and follow-up questions.

Controls, approvals, and delivery constraints

The work around the work.

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

  • Defined which views were approved for analysis and how each audience could use them.
  • Kept portfolio detail, user context, and source lineage visible instead of presenting an untraceable AI answer.
  • Designed the experience around authorized access, not public data exposure.

Public-safe outcome

A public-safe example of a portfolio intelligence workflow: explore the portfolio, inspect supporting detail, then ask a governed question.