• RANKING
  • PROBABILITY
  • GAPS
  • HUMAN

How to model applicant positioning with your AI

Connect your data once. Your agent then handles the comparison between individual candidates and the entire pool.

Ask AI about this page

Short answer

How can I see how one applicant compares to the rest of the pool?

Your agent uses competitive positioning to map a single applicant's attributes against the distribution of the whole group. This shows you exactly where they sit in the quality hierarchy. You get a clear view of their selection probability.

The outcomes of positioning analysis

Where your insights land.

The AI processes your applicant data to produce these specific results.

RANKING

Relative quality tiers

The AI places applicants into specific tiers based on their standing in the pool. You see exactly where a candidate sits in the hierarchy.

PROBABILITY

Selection likelihood

The agent calculates the chance of an applicant being selected based on pool density. This helps you identify high-probability candidates.

GAPS

Competitive advantages

The AI identifies which specific traits make an applicant stand out or fall behind. You get a list of the attributes driving their position.

HUMAN

Manual review flags

The AI flags borderline cases that need a person to look at them. You use these flags to decide which files to vet manually.

The workflow

What your AI does when the data arrives.

The agent moves from raw applicant data to specific positioning metrics.

  1. Data aggregation

    The agent pulls all relevant applicant profiles and historical selection data into its context.

    analyze_competitive_positioning

  2. Tier calculation

    The agent calculates how each applicant's metrics stack up against the mean of the pool.

    analyze_competitive_positioning

  3. Positioning mapping

    The AI maps these scores to specific quality tiers to show the distribution.

    analyze_competitive_positioning

  4. Probability estimation

    The agent translates these tiers into a likelihood score for selection.

    analyze_competitive_positioning

Try it

Copy these to start.

Use these prompts to trigger the analysis immediately.

Starting points

These are opening lines. Swap out the bracketed text for your actual file names or sheet IDs.

Accelerator Analytics Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_wYJDajtH9GeGtCIGxFdOc9FdE4efRvKvU1vJXL8U/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 — Accelerator Analytics Engine capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "accelerator-acceptance-analytics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_wYJDajtH9GeGtCIGxFdOc9FdE4efRvKvU1vJXL8U/mcp"
    }
  }
}
Copy into chat04

  • Analyze the competitive positioning for the applicants in 'Spring_Cohort_2024.csv'.

  • How does the candidate in 'application_id_882' compare to the rest of the pool in my Google Sheet?

  • Run a positioning analysis on the applicants listed in the 'Accelerator_Applicants' folder.

  • Based on the current pool in 'recruitment_data.json', what is the selection likelihood for the top-tier candidates?

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Accelerator Acceptance Analytics once, then ask.

Link your data source once. Your credentials stay encrypted, and you can immediately start asking your agent questions about your applicants.

Connect Accelerator Acceptance Analytics to your AI

FAQ

How this task behaves

  • 01

    Can the AI change my original applicant data?

    No. The AI only reads your data to perform analysis; it cannot modify your source files.

  • 02

    Does the AI make the final selection decisions?

    No. The AI provides likelihood scores and tiers, but the final decision to select or reject stays with you.

  • 03

    Can the AI delete my application records?

    No. This MCP is for read-only analytical tasks and has no capability to delete your records.

  • 04

    What data does the AI need to run this?

    The AI needs the applicant profiles and the pool data you provide through the connector.

  • 05

    Can I ask about specific candidates?

    Yes. Once the data is connected, you can ask about individual applicants or the group as a whole.

  • More questions about Accelerator Acceptance Analytics? The Connector page answers them. See everything the Accelerator Acceptance Analytics Connector can do

Connect Accelerator Analytics Engine to Claude, Cursor, ChatGPT & more