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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

Rate recommendation workflow.

A controlled recommendation workflow that gives client-facing teams a clearer, evidence-backed view before rate conversations and negotiations.

Pricing dataClient planningRecommendation workflowControls
Ask the project copilotGrounded in this public-safe case study.

Business problem

What people needed to solve.

Rate and negotiation teams need a consistent way to combine commercial history, planning assumptions, and client context before making a recommendation.

Decision frame

Questions the work needed to answer.

  • What recommendation is appropriate for this client situation?
  • Which assumptions and constraints shape the recommendation?
  • What needs commercial review before a number is presented?
Illustrative operating flowNo internal data, configuration, or client detail.
Commercial historyPlanning assumptionsClient context

Operating layer

Recommendation reviewDecision support with review and ownership.
Negotiation briefApproval pointClient conversation

Implementation

From the business problem to a working operating model.

Created a rate-recommendation flow that organizes inputs, constraints, review points, and client-facing negotiation preparation.

  1. 01

    Structured commercial inputs and planning assumptions into a common decision frame.

  2. 02

    Made constraints and supporting rationale explicit before recommendation review.

  3. 03

    Prepared a workflow for review, negotiation support, and controlled handoff to client teams.

Controls, approvals, and delivery constraints

The work around the work.

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

  • Recommendations are reviewable decision support, not automatic pricing.
  • Assumptions, exceptions, and approval points are recorded before a client-facing action.
  • Sensitive client detail is intentionally excluded from the public portfolio.

Public-safe outcome

A clearer path from commercial context to a reviewed recommendation and negotiation-ready narrative.