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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.

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All enterprise work

Decision intelligence 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.

Client-facing teams prepare rate and negotiation positions from commercial history, planning assumptions, and account context that live in separate places, so recommendations vary by preparer and are hard to defend in review. Without a shared decision frame, deal desks spend more time assembling inputs than pressure-testing the number.

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?
Interactive operating flowSelect any node for context or ask the AI guide.

Selected Operating layer

Recommendation review

Built a rate-recommendation workflow that assembles commercial inputs and planning assumptions into one structured view, makes constraints and rationale explicit, and routes each recommendation through commercial review before it reaches a client team.

Implementation

From the business problem to a working operating model.

Built a rate-recommendation workflow that assembles commercial inputs and planning assumptions into one structured view, makes constraints and rationale explicit, and routes each recommendation through commercial review before it reaches a client team.

  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.

Prior art

How leading teams approach this.

Public references for the same class of problem, so this work can be read against how other organizations are building it.

Anaplan

Infinera Analyzes Complex Deals at Lightning Speed with Anaplan

Infinera runs more than 12,000 annual pricing requests through a single deal desk model that standardizes cost data and approvals, the same pattern of turning scattered commercial inputs into one reviewable recommendation frame.

Anthropic

Claude for Financial Services

Claude for Financial Services centers on analysis with source hyperlinks and full audit trails, matching the requirement that a rate recommendation stay traceable decision support rather than an automatic price.

Public-safe outcome

Recommendations move from ad hoc preparation to a consistent, reviewable path where assumptions, exceptions, and approvals are recorded before any client-facing number is presented.