What does 'AI improvement velocity' actually measure?

It measures the speed and effectiveness of your product's ability to incorporate user feedback and deploy model upgrades. It gives you a single score that summarizes the health of your entire feedback loop.

Do I need to manually feed the data into the MCP?

No. The MCP connects to your existing data sources to calculate metrics like feedback efficiency and latency. You just need to ask your agent for the specific analysis you want.

Can this help me prove ROI on my ML team?

Yes. By correlating model changes with user sentiment and calculating the implementation rate, you get hard numbers that prove your development efforts are moving the product forward.

What is the difference between efficiency and latency?

Efficiency measures how well you turn feedback into upgrades. Latency measures how long it takes to get that upgrade out the door. Both are critical to product speed.

How is the velocity score calculated?

The velocity score is a composite metric that reflects the interplay between implementation speed and the quality of the feedback addressed using analyze_improvement_latency and calculate_feedback_efficiency logic.

Can I filter the summary for a specific feedback entry?

Yes, you can use the get_velocity_summary capability and provide a specific feedbackId to filter the results.

How does this capability help with user satisfaction?

By using get_satisfaction_metrics, you can correlate model improvement rates with implementation percentages to identify trends in user sentiment.

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Connect AI Improvement Velocity Tracker to your AI

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. Know exactly how fast your product is evolving.

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 Improvement Velocity Tracker.

01
Get satisfaction metrics
Use this to correlate specific model improvements with changes in overall user sentiment.
02
Get velocity summary
Get a high-level, immediate overview of your current AI product improvement performance and velocity score.
03
Analyze improvement latency
Measure the exact time delay in your improvement loop, from feedback collection to model deployment.
04
Calculate feedback efficiency
This capability analyzes how well your team converts raw user feedback into actual model upgrades, giving you an efficiency index.

Real work

What teams actually run

Post-Launch Review

After a major feature release, run the velocity summary to determine if the new model improved the overall product speed.

Resource Allocation

If the improvement latency is high, you can use the data to argue for dedicated resources to speed up the deployment pipeline.

Prioritizing Fixes

By checking feedback efficiency, you can determine which types of user complaints yield the highest return on development time.

Quarterly Reporting

Generate reports showing the correlation between model updates and positive shifts in user sentiment.

One connector, every AI

AI Improvement Velocity Tracker 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 Improvement Velocity Tracker 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

879ms average. Fast in production.

AI Improvement Velocity Tracker is checked daily against the live service.

Daily averagePeak 1061ms

Oct 3Today

Fastest day: 707ms
Slowest day: 1061ms
14-day trend: Improving-14%

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 Improvement Velocity Tracker, so you can see the experience inside your AI.

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

AI Improvement Velocity Tracker Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_dQzng4QoJaqbS57UQHtGMuMs1WvepoF2N1hAtXhx/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 Improvement Velocity Tracker capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-improvement-velocity-tracker-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_dQzng4QoJaqbS57UQHtGMuMs1WvepoF2N1hAtXhx/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 Improvement Velocity Tracker in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Improvement Velocity Tracker owners hand off.

This MCP is essential for product teams that rely on continuous AI iteration. If your job involves managing the lifecycle of a machine learning product, this capability gives you the quantitative data you need to prove product value and justify resource allocation.

01
Product Manager
Use this to report on the speed and efficiency of the product's iterative development cycle.
02
ML Engineer
Use this to benchmark the performance of the feedback loop and identify bottlenecks in the deployment process.
03
Product Owner
Use this to correlate specific model updates with measurable changes in user satisfaction and adoption.

FAQ

Questions AI Improvement Velocity Tracker owners ask.

01
What does 'AI improvement velocity' actually measure?
It measures the speed and effectiveness of your product's ability to incorporate user feedback and deploy model upgrades. It gives you a single score that summarizes the health of your entire feedback loop.
02
Do I need to manually feed the data into the MCP?
No. The MCP connects to your existing data sources to calculate metrics like feedback efficiency and latency. You just need to ask your agent for the specific analysis you want.
03
Can this help me prove ROI on my ML team?
Yes. By correlating model changes with user sentiment and calculating the implementation rate, you get hard numbers that prove your development efforts are moving the product forward.
04
What is the difference between efficiency and latency?
Efficiency measures how well you turn feedback into upgrades. Latency measures how long it takes to get that upgrade out the door. Both are critical to product speed.
05
How is the velocity score calculated?
The velocity score is a composite metric that reflects the interplay between implementation speed and the quality of the feedback addressed using analyze_improvement_latency and calculate_feedback_efficiency logic.
06
Can I filter the summary for a specific feedback entry?
Yes, you can use the get_velocity_summary capability and provide a specific feedbackId to filter the results.
07
How does this capability help with user satisfaction?
By using get_satisfaction_metrics, you can correlate model improvement rates with implementation percentages to identify trends in user sentiment.

Explore

More in Product Management

Connect AI Improvement Velocity Tracker 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

Get satisfaction metrics

Get velocity summary

Analyze improvement latency

Calculate feedback efficiency

What teams actually run

Post-Launch Review

Resource Allocation

Prioritizing Fixes

Quarterly Reporting

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

AI Improvement Velocity Tracker Connector

Claude Desktop

Product Manager

ML Engineer

Product Owner

What does 'AI improvement velocity' actually measure?

Do I need to manually feed the data into the MCP?

Can this help me prove ROI on my ML team?

What is the difference between efficiency and latency?

How is the velocity score calculated?

Can I filter the summary for a specific feedback entry?

How does this capability help with user satisfaction?

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