Is this just a calculator, or does it provide strategic advice?

It's both. While it calculates specific metrics like infrastructure cost and latency impact, it also uses those numbers to provide strategic advice on where you should invest or cut spending.

Does it account for embeddings and storage costs?

Yes. The MCP calculates the total monetary cost, specifically detailing the monthly spend required for both storage and embeddings.

Can I use this for different types of search enhancements?

You can model enhancements like semantic search, reranking, and hybrid search to see their specific impact on relevance and performance.

What kind of data does it predict?

It predicts three main things: the total monetary cost, the increase in user latency, and the quality lift, measured as a relevance score improvement.

How does this capability calculate infrastructure costs?

The calculate_infrastructure_investment capability calculates costs by summing the storage for the index, the cost of generating embeddings for the specified index size, and the compute costs for processing the monthly search volume.

Can I predict the latency of adding a reranker?

Yes, you can use estimate_latency_impact to predict how much of the user's time budget will be consumed by adding features like reranking or hybrid search.

What is the purpose of the optimization capability?

The get_optimization_recommendations capability provides strategic advice to help you decide whether to cut costs or invest more to improve performance based on your current metrics.

An astronaut overseeing a futuristic data hub

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Connect AI Search Investment 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 search improvements are worth the investment.

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.

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.

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 Search Modeler.

01
Calculate infrastructure investment
Determines the total monetary cost of the search setup
02
Estimate latency impact
Predicts how much of the user's time budget will be consumed by the proposed search stack
03
Evaluate relevance gain
Calculates the estimated value or quality lift provided by the enhancements
04
Get optimization recommendations
Provides strategic advice on where to cut costs or where to invest for better performance

Real work

What teams actually run

Launching Semantic Search

Before rolling out semantic search, use the MCP to calculate the required embedding costs and potential relevance boost.

Optimizing Existing Search

Test if adding a reranking layer is worth the extra latency and compute cost compared to the relevance lift.

Budget Planning

Model the total infrastructure investment required for a high-volume search system over a fiscal quarter.

Feature Comparison

Compare the cost and performance of hybrid search versus pure keyword search to justify the upgrade.

One connector, every AI

AI Search 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.

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

You don't need a client to put AI Search 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

901ms average. Fast in production.

AI Search Modeler is checked daily against the live service.

Daily averagePeak 971ms

Oct 3Today

Fastest day: 696ms
Slowest day: 971ms
14-day trend: Stable+3%

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 Search Modeler, so you can see the experience inside your AI.

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

AI Search Modeler Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_4sybgVVU9LYndPjDU66KffbBh9Hw9nUIhzmV6qOz/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — AI Search Modeler capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-search-investment-modeler-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_4sybgVVU9LYndPjDU66KffbBh9Hw9nUIhzmV6qOz/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 Search Modeler in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Search Modeler owners hand off.

This MCP is built for technical leaders and product architects who manage search infrastructure. If you need to justify the budget for AI-powered search features, this capability gives you the hard numbers you need.

01
Product Manager
Use this to estimate the user value (relevance gain) versus the cost of a new feature.
02
Solutions Architect
Determine the optimal balance between latency, cost, and performance for a given use case.
03
CTO/VP of Engineering
Get a clear view of the total cost of ownership (TCO) for advanced search systems.

FAQ

Questions AI Search Modeler owners ask.

01
Is this just a calculator, or does it provide strategic advice?
It's both. While it calculates specific metrics like infrastructure cost and latency impact, it also uses those numbers to provide strategic advice on where you should invest or cut spending.
02
Does it account for embeddings and storage costs?
Yes. The MCP calculates the total monetary cost, specifically detailing the monthly spend required for both storage and embeddings.
03
Can I use this for different types of search enhancements?
You can model enhancements like semantic search, reranking, and hybrid search to see their specific impact on relevance and performance.
04
What kind of data does it predict?
It predicts three main things: the total monetary cost, the increase in user latency, and the quality lift, measured as a relevance score improvement.
05
How does this capability calculate infrastructure costs?
The calculate_infrastructure_investment capability calculates costs by summing the storage for the index, the cost of generating embeddings for the specified index size, and the compute costs for processing the monthly search volume.
06
Can I predict the latency of adding a reranker?
Yes, you can use estimate_latency_impact to predict how much of the user's time budget will be consumed by adding features like reranking or hybrid search.
07
What is the purpose of the optimization capability?
The get_optimization_recommendations capability provides strategic advice to help you decide whether to cut costs or invest more to improve performance based on your current metrics.

Explore

More in Finance

Connect AI Search Modeler to Claude, Cursor, ChatGPT & more

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

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Calculate infrastructure investment

Estimate latency impact

Evaluate relevance gain

Get optimization recommendations

What teams actually run

Launching Semantic Search

Optimizing Existing Search

Budget Planning

Feature Comparison

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

AI Search Modeler Connector

Claude Desktop

Product Manager

Solutions Architect

CTO/VP of Engineering

Is this just a calculator, or does it provide strategic advice?

Does it account for embeddings and storage costs?

Can I use this for different types of search enhancements?

What kind of data does it predict?

How does this capability calculate infrastructure costs?

Can I predict the latency of adding a reranker?

What is the purpose of the optimization capability?

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