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Hire and Retain Top Talent

Create digital twins of candidates and employees. Predict their responses before critical conversations. Build stronger teams.

Mind Reasoner

The Paradigm Shift

Stop guessing in high-stakes HR decisions. Create minds of candidates and employees. Predict exactly how they’ll respond—before offers, performance conversations, and retention discussions.

Result: Hire better talent and retain your best people.

Why HR Decisions Fail

The Problem

You’re making critical decisions blind:

  • Will this candidate accept our offer?
  • Is this employee about to leave?
  • How will they respond to feedback?
  • What keeps our top performers here?

You’re guessing. And losing talent because of it.

The Solution

Make decisions knowing:

  • Exactly what motivates this candidate
  • Which employees are flight risks
  • How to frame difficult conversations
  • What retains your best people

No more guessing. Just better talent decisions.

How It Works

Upload interview transcripts, 1-on-1 notes, or conversation records

Training: 5-15 minutes per person

Result: Digital twins that think and respond like your candidates and employees

Ask the mind any question:

  • “Will you accept our offer at this salary?”
  • “Are you considering leaving? What keeps you here?”
  • “How will you respond to this feedback?”

Result: Know exactly what they’ll say before the conversation

Enter conversations prepared:

  • Right offer for THIS candidate
  • Retention approach for THIS employee
  • Feedback framing that resonates

Result: Hire and retain significantly more top talent

What You Can Predict

Candidate Responses

Know if candidates will accept before making offers

Predict:

  • Salary expectations and negotiation
  • What motivates them most
  • Competing offers and decision factors
  • Cultural fit and concerns

Outcome: Win top talent with tailored offers

Employee Retention

Identify flight risk and address concerns proactively

Predict:

  • Who’s considering leaving
  • What keeps them vs. what drives them away
  • Response to retention conversations
  • Career development needs

Outcome: Retain your best people

Performance Conversations

Know how employees will respond to feedback

Predict:

  • How they’ll receive critical feedback
  • What framing resonates with them
  • Defensive vs. receptive responses
  • What motivates improvement

Outcome: Have more effective performance conversations

Team Dynamics

Predict team reactions to changes and conflicts

Predict:

  • Responses to organizational changes
  • Conflict resolution approaches
  • Team morale impact
  • Who needs proactive conversations

Outcome: Navigate team challenges successfully

Quick Start

Set up MCP Server (5 minutes, no coding):

MCP Quick Start →

In Claude:

> "Create a mind for [Candidate/Employee Name],
using /path/to/interview-transcript.vtt"

Wait 5-15 minutes for training

> "Will you accept our offer at $150K?
What concerns do you have about joining?"

Get predictions. Tailor your approach. Win the talent.

Implementation

Use Mind Reasoner through Claude Code

No coding. Just natural conversation in Claude.

You:

> Create a mind for Sarah Kim, Senior Engineer candidate,
using /Documents/interviews/sarah-kim-interview.vtt

Mind Reasoner:

✓ Created mind: "Sarah Kim - Senior Engineer Candidate"
✓ Training... (~10 min)

You:

> Will Sarah accept our offer at $150K base + $50K equity?
What concerns will she raise? What motivates her most?

Mind Reasoner:

Based on Sarah's interview patterns:
OFFER ACCEPTANCE: UNLIKELY at this package
Her priorities:
1. Compensation Gap: "$150K is below market"
   Context: She mentioned current offers at $165K
   Risk: Will use your offer as leverage elsewhere
   What wins her: $165K+ to match competitive offers
2. Equity Value: "How much is equity actually worth?"
   Context: Skeptical about startup equity
   Risk: Discounts equity value significantly
   What wins her: More cash, less equity weighting
3. Technical Growth: "Will I work on cutting-edge problems?"
   Context: This is her #1 motivation (not comp)
   Risk: Competing offer has more interesting tech
   What wins her: Emphasize technical challenges + autonomy
Revised offer that wins her:
- $165K base (matches market)
- $30K equity (reduce since she discounts it)
- Lead role on AI infrastructure project
- Framed as "technical growth opportunity"

See All MCP Tools →

Next Steps

Predict Candidate Responses

Win top talent with offers tailored to what they actually care about

Strengthen Employee Engagement

Retain your best people by addressing concerns proactively

MCP Quick Start

Set up in 5 minutes. No coding required.

API Quick Start

Build talent prediction into your HR systems

Questions? Email support@mindreasoner.com