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Connect AI Personalization ROI Modeler to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Determine if your next feature is financially viable.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Personalization ROI Modeler.

  1. 01

    Calculate personalization roi

    Provides a high-level summary of the financial viability of a personalization feature

  2. 02

    Estimate privacy overhead

    Determines the additional cost impact caused by different regulatory environments

  3. 03

    Project revenue growth

    Calculates how much revenue will increase over a specific time horizon

  4. 04

    Validate feature feasibility

    Checks if a proposed personalization feature meets minimum viability thresholds

Real work

What teams actually run

01

Launching a New Feature

Before coding, run the model to see if a 5% conversion lift on your current revenue base hits your minimum ROI target.

02

Compliance Planning

If you're expanding into a new region, use the overhead capability to calculate the exact cost of meeting local privacy laws.

03

Budget Justification

Present a full financial model showing projected revenue growth over three years to secure funding for a personalization initiative.

04

Portfolio Review

Run multiple features through the feasibility checker to quickly identify which ideas are financially impossible.

One connector, every AI

AI Personalization ROI Modeler works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put AI Personalization ROI Modeler to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

853ms average. Fast in production.

AI Personalization ROI Modeler is checked daily against the live service.

Daily averagePeak 965ms
Oct 3Today
Fastest day
639ms
Slowest day
965ms
14-day trend
Slowing+13%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AI Personalization ROI Modeler, so you can see the experience inside your AI.

It does not authenticate your account with AI Personalization ROI Modeler. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI Personalization ROI Modeler Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_CVkMrjXBj00HeoBq95I3ucKqiOKbTxjLX6lWon7Y/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — AI Personalization ROI Modeler capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-personalization-roi-modeler-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_CVkMrjXBj00HeoBq95I3ucKqiOKbTxjLX6lWon7Y/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up AI Personalization ROI Modeler in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Personalization ROI Modeler owners hand off.

This MCP is built for product leaders and finance teams who need to prove the financial value of digital initiatives. If you're responsible for making sure new features actually hit revenue targets, this capability gives you the numbers you need to get buy-in.

  • 01

    Product Manager

    Use it to validate feature ideas and determine if the potential conversion lift justifies the development cost.

  • 02

    CMO / Marketing Director

    Use it to model the expected revenue growth from personalization campaigns before launching them.

  • 03

    Financial Analyst

    Use it to calculate the total investment, including complex overhead like privacy compliance.

FAQ

Questions AI Personalization ROI Modeler owners ask.

  • 01

    Does this MCP account for privacy costs?

    Yes. The estimate_privacy_overhead capability specifically determines the additional cost impact caused by different regulatory environments, ensuring your total investment is accurate.

  • 02

    Can I project revenue for more than one year?

    The project_revenue_growth capability handles long-term projections. You can input your base revenue and desired time horizon to see the total projected lift.

  • 03

    What kind of data do I need to start?

    You need core metrics like your current monthly revenue, the expected conversion lift from the feature, and the infrastructure costs associated with it.

  • 04

    Is this capability just for revenue lift?

    No. It's broader. You can also use it to validate if a feature meets minimum viability thresholds, which is a key step before calculating the ROI.

  • 05

    Do I need to write complex formulas?

    No. You just tell your AI client what you want to calculate—like the ROI—and the MCP handles the complex financial modeling behind the scenes.

  • 06

    How does privacy complexity affect the investment?

    Higher privacy complexity (e.g., 'high' for strict data sovereignty) increases the total investment by adding a multiplier to the base infrastructure cost via the estimate_privacy_overhead logic.

  • 07

    Can I project revenue over multiple years?

    Yes, you can use project_revenue_growth and specify the desired number of months in the timeframeMonths parameter to see cumulative lift.

  • 08

    What defines a 'viable' feature?

    A feature is considered viable if its payback period is within your specified threshold, which can be checked using validate_feature_feasibility.

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