EffectvHire Product Documentation

Version 1.0

Decision Intelligence for High-Stakes Hiring

Getting Started
Core Concepts
Creating Your SkillDNA
Screening Candidates
Planning Interviews
Conducting Interviews
Making Hiring Decisions
AI Copilot (Optional)
Integrations
Best Practices
Troubleshooting

Getting Started

Welcome to EffectvHire

EffectvHire transforms senior leadership hiring from subjective guesswork into structured, defensible decisions. When you’re hiring Principals, Directors, and VPs—roles where one mis-hire costs $500K-$2M+—you need decision intelligence, not just process tracking.

What EffectvHire Does

Defines Success Before You Start: Creates precise SkillDNA for each role
Screens for What Matters: Matches candidates against skills, not keywords
Structures Multi-Round Interviews: Ensures every skill is assessed systematically
Enables Data-Driven Decisions: Provides side-by-side candidate comparisons with evidence

System Requirements

Modern web browser (Chrome, Firefox, Safari, Edge)
Active ATS account (Greenhouse, Lever, or compatible system)
Stable internet connection

Initial Setup

Log in to EffectvHire at https://www.effectv.ai
Connect your ATS: Navigate to Settings → Integrations → Select your ATS
Add team members: Settings → Team → Invite interviewers and coordinators
Set permissions: Assign roles (Admin, Hiring Manager, Interviewer, Coordinator)

Core Concepts

The EffectvHire Workflow

1. CREATE SKILLDNATM → 2. SCREEN CANDIDATES → 3. PLAN INTERVIEWS →

4. CONDUCT & EVALUATE → 5. COMPARE & DECIDE

Key Terminology

SkillDNA: The precise, multi-dimensional skill framework that defines what “good” looks like for a specific role. Goes beyond generic job descriptions to capture technical depth, leadership capabilities, and domain expertise required in YOUR context.

Skill Bucket: A category of related competencies (e.g., “Technical Architecture,” “Stakeholder Management,” “Team Leadership”).

Proficiency Level: A 1-10 scale indicating required mastery for each skill. We define what different levels mean for your specific context.

Interview Plan: The strategic distribution of skill assessments across multiple interview rounds, ensuring complete coverage without redundancy.

Assessment Rubric: Specific criteria for evaluating a candidate’s proficiency in each skill dimension.

Decision Dashboard: Side-by-side candidate comparisons showing evidence-based skill ratings across all dimensions.

User Roles

Admin: Full system access, manages team and settings
Hiring Manager: Creates SkillDNA, reviews candidates, makes final decisions
Interviewer: Conducts interviews and submits evaluations
Coordinator: Schedules interviews, manages candidate flow
Recruiter: Sources candidates, manages initial screening

Creating Your SkillDNA

Overview

SkillDNA is the foundation of your structured hiring process. It answers: “What specific skills and experiences predict success in this role, in our organization?”

Step 1: Start a New Role

Navigate to Roles → Create New Role
Enter basic information:
- Role title (e.g., “VP Engineering - Platform”)
- Department
- Reporting structure
- Compensation range
- Target start date

Step 2: Upload or Paste Job Description

Click Generate SkillDNA
Choose one of three options:
- Upload JD file (PDF, Word, or text)
- Paste JD text directly
- Start from scratch (AI-guided interview)

Step 3: AI-Powered Skill Extraction

EffectvHire’s AI analyzes your job description and extracts:

Technical Skills: Specific technologies, methodologies, domain knowledge
Leadership Skills: Team management, strategic thinking, decision-making
Domain Expertise: Industry knowledge, functional experience
Cross-Functional Skills: Collaboration, communication, influence

Processing time: 2-5 minutes

Step 4: Review and Customize SkillDNA

The AI generates an initial SkillDNA. Now you customize it:

4a. Review Skill Buckets

Example output for “VP Engineering - Platform”:

TECHNICAL ARCHITECTURE (Required: 9/10)

- Design scalable distributed systems

- Cloud infrastructure expertise (AWS/GCP/Azure)

- Security and compliance architecture

- API design and microservices

ENGINEERING LEADERSHIP (Required: 9/10)

- Manage teams of 30-50+ engineers

- Build and scale engineering processes

- Attract and retain top technical talent

- Performance management and coaching

PRODUCT & STRATEGY (Required: 8/10)

- Translate business goals into technical roadmaps

- Balance tech debt vs. feature velocity

- Cross-functional collaboration with Product/Design

- Data-driven decision making

STAKEHOLDER MANAGEMENT (Required: 8/10)

- Executive communication and influence

- Board-level technical presentations

- Navigate organizational politics

- Build trust across departments

4b. Adjust Proficiency Levels

For each skill bucket, define what proficiency means:

1-3/10: Basic familiarity, requires significant support
4-6/10: Competent, can execute with guidance
7-8/10: Strong, can lead initiatives independently
9-10/10: Expert, can define strategy and mentor others

Click Edit Proficiency Definitions to customize what each level means in your context.

4c. Prioritize Skills

Mark skills as:

Must-Have: Non-negotiable, candidate must meet minimum threshold
Nice-to-Have: Valuable but can be developed
Differentiator: Rare combinations that create exceptional fit

4d. Add Context and Trade-offs

Document key decisions:

“We’re prioritizing distributed systems expertise over specific language experience”
“Leadership experience matters more than hands-on coding at this level”
“Enterprise sales cycle knowledge is a differentiator given our customer base”

Why this matters: When comparing candidates later, you’ll reference these decisions to resolve trade-offs.

Step 5: Collaborate and Finalize

Share with stakeholders: Click Share for Review
Collect feedback: Stakeholders can comment on specific skills
Iterate: Incorporate feedback and refine
Finalize: Click Lock SkillDNA when consensus is reached

Best Practice: Include your hiring manager, cross-functional partners, and 2-3 senior team members in the review. Alignment here prevents debate later.

Step 6: Save as Template (Optional)

If you hire similar roles frequently:

Click Save as Template
Name it (e.g., “VP Engineering - Standard”)
Reuse and customize for future searches

Screening Candidates

Overview

EffectvHire matches candidate resumes and profiles against your SkillDNA—not against keywords. You’ll see skill-by-skill scoring with evidence and explanations.

Step 1: Import Candidates

Three ways to add candidates:

Option A: ATS Sync

Navigate to Candidates → Sync from ATS
Select the job requisition
Click Import Candidates
EffectvHire automatically pulls resumes and application data

Option B: Manual Upload

Navigate to Candidates → Add Candidate
Upload resume (PDF, Word, LinkedIn profile)
Enter basic info (name, email, current role)

Option C: Bulk Upload

Navigate to Candidates → Bulk Import
Upload CSV with candidate information
Attach resumes as ZIP file

Step 2: AI-Powered Skill Matching

Once imported, EffectvHire automatically:

Analyzes each resume against your SkillDNA
Extracts relevant experience and evidence
Generates skill-by-skill scores with explanations

Processing time: 1-3 minutes per candidate

Step 3: Review Candidate Profiles

Click on any candidate to see their Skill Match Report:

Example: Sarah Chen - VP Engineering Candidate

OVERALL MATCH: 82% (Strong Fit)

TECHNICAL ARCHITECTURE (Your requirement: 9/10)

Candidate Rating: 8.5/10

Evidence:

- Led migration to microservices architecture at DataCorp (500+ engineers)

- Designed multi-region AWS infrastructure handling 10M+ daily transactions

- Security: Achieved SOC 2 and ISO 27001 certifications

- 12 years experience in distributed systems

Gap Analysis: Strong technical depth. Minor gap: Limited GCP experience (mostly AWS).

ENGINEERING LEADERSHIP (Your requirement: 9/10)

Candidate Rating: 9/10

Evidence:

- Managed team of 45 engineers across 4 locations

- Scaled team from 12 to 45 in 18 months with 92% retention

- Implemented OKR framework and improved deployment frequency 10x

- Multiple references cite exceptional mentorship and culture-building

Gap Analysis: Exceeds requirements. Demonstrated ability to scale teams.

PRODUCT & STRATEGY (Your requirement: 8/10)

Candidate Rating: 7/10

Evidence:

- Partnered with Product on 3-year technical roadmap

- Led platform modernization initiative ($15M investment)

- Limited direct P&L ownership

Gap Analysis: Solid product collaboration. Less experience with business strategy and revenue ownership than ideal.

STAKEHOLDER MANAGEMENT (Your requirement: 8/10)

Candidate Rating: 8/10

Evidence:

- Regular board presentations on technical strategy

- Cross-functional leadership of 5-department initiative

- Strong reputation for building trust with non-technical stakeholders

Gap Analysis: Meets requirements. Strong executive presence.

Step 4: Shortlist Candidates

Use filters and sorting:

Minimum Overall Match: Set threshold (e.g., 75%+)
Must-Have Skills: Filter by specific skill requirements
Experience Level: Years of experience, team size managed
Industry Background: Relevant domain expertise

Actions:

✅ Shortlist: Move to interview stage
⏸️ Maybe: Need more information or time
❌ Decline: Not a fit, send rejection

Step 5: Compare Candidates Side-by-Side

Select 2-4 candidates and click Compare:

Skill Dimension

Sarah Chen

Michael Rodriguez

Priya Sharma

Technical Architecture

8.5/10 ✅

7/10 ⚠️

9/10 ✅

Engineering Leadership

9/10 ✅

8/10 ✅

7.5/10 ⚠️

Product & Strategy

7/10 ⚠️

9/10 ✅

8/10 ✅

Stakeholder Management

8/10 ✅

8.5/10 ✅

7/10 ⚠️

Overall Match

82%

81%

79%

Trade-off Summary:

Sarah: Strongest leadership, minor product/strategy gap
Michael: Best product/strategy, weaker technical depth
Priya: Strongest technical, needs more leadership scale experience

Best Practices for Screening

Don’t over-rely on overall match percentage: Dig into specific skills
Consider compensation alignment: Factor in candidate expectations
Review the evidence: Don’t just trust the score—read the supporting details
Involve hiring manager early: Get their input on top 5-7 candidates
Document why you’re declining: Helps refine SkillDNA for future searches

Planning Interviews

Overview

Interview planning ensures every critical skill is assessed at least once, distributed strategically across multiple rounds, without redundancy or gaps.

Step 1: Select Candidate for Interview Planning

Navigate to Candidates → Shortlisted
Click on candidate name
Click Create Interview Plan

Step 2: Define Interview Structure

Specify your interview process:

Number of Rounds: 4-10 rounds (typical for senior leadership)

Interview Types:

Phone Screen (30 min)
Technical Deep Dive (60-90 min)
Leadership & Culture (60 min)
Cross-Functional Stakeholder Round (45-60 min)
Executive Round (45 min)
Team Meet (optional, 30-45 min)

Interviewers: Assign specific people to each round

Step 3: AI-Generated Interview Plan

EffectvHire automatically:

Distributes skill assessments across rounds based on interviewer expertise
Balances load so no interviewer assesses more than 3-4 skills
Assigns specific questions for each skill in each round
Tracks coverage to ensure no skill is missed

Example Interview Plan: Sarah Chen

ROUND 1: Phone Screen (Recruiter: Alex Kim - 30 min)

Focus: Motivation, compensation alignment, logistics

Skills Assessed: None (qualification only)

ROUND 2: Technical Deep Dive (Engineering Manager: Jamie Torres - 90 min)

Primary Skills Assessed:

- Technical Architecture (9/10 required)

- Engineering Leadership - Process & Systems (9/10 required)

Questions:

1. Walk me through the most complex distributed system you've designed.

What were the trade-offs?

2. Describe your approach to balancing technical debt vs. feature velocity.

3. How do you ensure security and compliance in a fast-moving environment?

ROUND 3: Leadership & Strategy (VP Engineering: Current - 60 min)

Primary Skills Assessed:

- Engineering Leadership - People Management (9/10 required)

- Product & Strategy (8/10 required)

Questions:

1. Tell me about a time you scaled a team from 15 to 50+. What worked? What didn't?

2. How do you translate business objectives into technical roadmaps?

3. Describe your approach to performance management and coaching.

ROUND 4: Cross-Functional (VP Product: Dana Lee - 60 min)

Primary Skills Assessed:

- Product & Strategy - Collaboration (8/10 required)

- Stakeholder Management (8/10 required)

Questions:

1. Describe a situation where Engineering and Product disagreed. How did you resolve it?

2. How do you build trust with non-technical stakeholders?

3. Walk me through how you'd communicate a major technical decision to the executive team.

ROUND 5: Executive Round (CTO: Chris Martinez - 45 min)

Primary Skills Assessed:

- Strategic Vision (8/10 required)

- Stakeholder Management - Executive Presence (8/10 required)

Questions:

1. Where do you see platform engineering heading in the next 3-5 years?

2. How would you prioritize our technical initiatives given limited resources?

3. What questions do you have for me about our strategy and challenges?

ROUND 6: Team Meet (Optional - 45 min)

Focus: Culture fit, team dynamics, candidate questions

Skills Assessed: None (mutual evaluation)

Step 4: Customize Interview Plan

Review and adjust:

Reorder rounds if needed (drag and drop)
Reassign interviewers based on availability
Add or remove questions to match candidate background
Adjust time allocations per round

Step 5: Generate Interview Guides

For each round, EffectvHire generates a customized Interview Guide for the interviewer:

Example: Jamie Torres - Technical Deep Dive Guide

CANDIDATE: Sarah Chen

ROLE: VP Engineering - Platform

DATE: [Scheduled date/time]

DURATION: 90 minutes

YOUR FOCUS: Technical Architecture & Engineering Leadership (Process)

CANDIDATE BACKGROUND SUMMARY:

- Currently VP Engineering at DataCorp (SaaS company, 200 employees)

- Led team of 45 engineers

- Migrated monolith to microservices (AWS-based)

- Strong distributed systems background

- Limited GCP experience (mostly AWS)

SKILLS YOU'RE ASSESSING:

1. TECHNICAL ARCHITECTURE (Required: 9/10)

What to look for:

- Can they articulate complex system designs clearly?

- Do they consider trade-offs (cost, performance, maintainability)?

- Evidence of designing systems at scale (high throughput, reliability)

- Security and compliance awareness

2. ENGINEERING LEADERSHIP - PROCESS (Required: 9/10)

What to look for:

- Do they have a framework for balancing tech debt vs. features?

- Can they describe how they've scaled engineering processes?

- Evidence of data-driven decision making

Conducting Interviews

Overview

Interviewers use EffectvHire’s guides to conduct structured assessments, then log their evaluations immediately after each interview.

Before the Interview

Review the Interview Guide: 15-20 minutes before the interview
Check candidate background: Review their SkillDNA match report
Note what’s already been assessed: Review coverage tracker to avoid redundancy
Prepare your environment: Quiet space, stable internet (if virtual)

During the Interview

Timing:

5 min: Introductions, set context
60-70 min: Core assessment questions
10-15 min: Candidate questions
5 min: Closing and next steps

Best Practices:

Follow the guide, but adapt: If candidate reveals unexpected depth, dig deeper
Take brief notes: Capture evidence, not just impressions
Use follow-up questions: “Tell me more about…” “Walk me through…” “What would you do differently?”
Avoid leading questions: Let the candidate reveal their thinking
Watch for depth vs. breadth: Do they speak from experience or theory?

After the Interview

Complete Evaluation Within 24 Hours:

Navigate to My Interviews → [Candidate Name]
Click Submit Evaluation

Evaluation Form

Part 1: Skill Ratings

For each skill you assessed, provide:

Rating (1-10 scale):

Use the proficiency definitions from SkillDNA
Be honest and calibrated

Evidence (Required):

Specific examples from the interview
What the candidate said or described
Why you gave this rating

Example:

SKILL: Technical Architecture (Required: 9/10)

YOUR RATING: 8.5/10

EVIDENCE:

Sarah described designing a multi-region AWS architecture handling 10M+ transactions

daily with 99.99% uptime. She articulated clear trade-offs:

- Chose eventual consistency over strong consistency for performance

- Implemented circuit breakers and bulkheads for fault isolation

- Explained cost optimization strategies (reserved instances, autoscaling)

She demonstrated deep understanding of CAP theorem trade-offs and practical application.

Minor gap: Limited experience with GCP (our secondary cloud platform), but fundamentals

transfer easily.

Why 8.5 not 9: Slightly less experience with multi-cloud strategies, which we'll need

as we expand to GCP for specific workloads.

Part 2: Strengths & Concerns

Strengths:

What impressed you most?
Where did candidate exceed expectations?

Concerns:

Any red flags or gaps?
Areas where candidate struggled?

Example:

STRENGTHS:

- Exceptional clarity in explaining complex technical concepts

- Real-world battle scars from scaling systems (learned from failures)

- Humble and self-aware about gaps

- Asked insightful questions about our architecture and challenges

CONCERNS:

- Limited experience with GCP (mostly AWS)

- Hasn't worked in heavily regulated industry (we're in healthcare)

- May need support navigating complex compliance requirements initially

Part 3: Recommendation

✅ Strong Yes: Definitely move forward
✅ Yes: Positive, should move forward
⚠️ Maybe: On the fence, need more information
❌ No: Should not move forward
❌ Strong No: Definite red flags

Overall Comments:

Sarah is a strong candidate with excellent technical depth and leadership experience.

She's clearly led complex initiatives and has the scars to prove it. My only hesitation

is around healthcare compliance—she hasn't worked in this space before. But I think

her fundamentals are strong enough that she could learn quickly with support.

Recommendation: Move forward. This is someone who can execute at VP level.

Real-Time Coverage Tracking

As evaluations are submitted, EffectvHire updates the Coverage Dashboard:

SARAH CHEN - COVERAGE STATUS

✅ Technical Architecture (Assessed by Jamie Torres)

✅ Engineering Leadership - Process (Assessed by Jamie Torres)

✅ People Management (Assessed by VP Engineering)

✅ Product Strategy (Assessed by VP Engineering)

⏳ Stakeholder Management (Scheduled: VP Product, tomorrow)

⏳ Executive Presence (Scheduled: CTO, next week)

OVERALL COVERAGE: 67% (4 of 6 core skills assessed)

Best Practices for Evaluations

Submit within 24 hours: Memories fade quickly
Be specific with evidence: “Impressive communication” is useless; “Explained CAP theorem trade-offs clearly to non-technical stakeholders” is valuable
Calibrate with peers: If you’re consistently harder or easier than other interviewers, recalibrate
Focus on job-relevant skills: Don’t over-index on “likability”
Document concerns early: If you see red flags in Round 2, flag them so later interviewers can probe

Making Hiring Decisions

Overview

After all interviews are complete, EffectvHire’s Decision Dashboard provides side-by-side candidate comparisons with evidence-based skill ratings, enabling structured, defensible decisions.

Step 1: Access Decision Dashboard

Navigate to Roles → [Role Name]
Click Decision Dashboard
Select candidates to compare (typically final 2-4)

Step 2: Review Side-by-Side Comparison

Example: VP Engineering - Final 3 Candidates

SKILL DIMENSION | Sarah Chen | Michael Rodriguez | Priya Sharma

------------------------|------------|-------------------|-------------

TECHNICAL ARCHITECTURE | 8.5/10 ✅ | 7/10 ⚠️ | 9/10 ✅

Evidence Summary | AWS expert, scaled to 10M+ TPS | Solid, limited scale | GCP + AWS, distributed systems depth

ENGINEERING LEADERSHIP | 9/10 ✅ | 8/10 ✅ | 7.5/10 ⚠️

Evidence Summary | Scaled 12→45, 92% retention | Team of 30, good culture | Team of 15, less scale

PRODUCT & STRATEGY | 7/10 ⚠️ | 9/10 ✅ | 8/10 ✅

Evidence Summary | Product collaboration, no P&L | Owned $20M P&L, strong biz acumen | Strong product sense

STAKEHOLDER MANAGEMENT | 8/10 ✅ | 8.5/10 ✅ | 7/10 ⚠️

Evidence Summary | Board presentations, cross-func | Exceptional exec presence | Needs coaching on influence

OVERALL MATCH | 82% | 81% | 79%

COMPENSATION ALIGNMENT | $250K (in range) | $280K (high end) | $220K (below market)

Step 3: Review Interviewer Comments

Click on any candidate to see full evaluation details:

All interviewer ratings and evidence
Strengths and concerns from each round
Verbatim comments (searchable)

Step 4: Conduct Decision Debrief

Structure the meeting:

Attendees: Hiring manager, all interviewers, key stakeholders

Duration: 45-60 minutes

Agenda:

Review SkillDNA priorities (5 min)
- Remind everyone what we agreed “good” looks like
- Reiterate must-haves vs. nice-to-haves
Candidate-by-candidate review (10 min each)
- Overall match and key strengths
- Specific concerns or gaps
- Trade-offs vs. other candidates
Direct comparison (15 min)
- Where do candidates differ most?
- Which trade-offs matter for our specific context?
- Reference SkillDNA priorities and context
Decision (5-10 min)
- Rank order: Who’s #1? Who’s #2 (backup)?
- Any deal-breakers or concerns to address in offer stage?

Example Debrief Dialogue:

Hiring Manager: "Let's start with Sarah. Jamie, you did her technical deep dive—what stood out?"

Jamie (Interviewer): "She's an 8.5 on technical architecture. Excellent AWS depth,

scaled systems to 10M+ TPS, strong distributed systems fundamentals. My only concern

is limited GCP experience, but I think she'd ramp quickly."

VP Product: "I assessed her on stakeholder management. She's an 8—solid exec presence,

good board-level communication. She asked smart questions about our product strategy."

Hiring Manager: "The gap I see is product strategy—she's a 7, we wanted 8. She hasn't

owned a P&L. Thoughts?"

VP Engineering: "That's true, but she's partnered closely with Product at her current

company. And her leadership is a 9—that's exceptional. I think the product piece can

be coached."

Hiring Manager: "Now let's compare to Michael. He's a 9 on product strategy, owned a

$20M P&L. But he's a 7 on technical architecture—weaker than we'd like."

Jamie: "Yeah, Michael's not as deep technically. He can speak the language, but I

don't think he could architect complex systems himself. That concerns me given we're

rebuilding our platform."

Hiring Manager: "So the trade-off is: Sarah (stronger technical + leadership, weaker

product/biz) vs. Michael (stronger product/biz, weaker technical). Given our context

—we're re-architecting the platform and need someone who can lead that technically—

I'm leaning Sarah. We can surround her with product support."

[Discussion continues...]

Hiring Manager: "Okay, I'm hearing consensus around Sarah as #1, Michael as #2 backup.

Any deal-breakers or concerns we need to address in the offer conversation?"

VP Engineering: "Just the healthcare compliance piece—let's be upfront that she'll

need support there initially."

Hiring Manager: "Agreed. Let's extend an offer to Sarah, with Michael as backup if

negotiations don't work out."

Step 5: Document Decision Rationale

EffectvHire captures:

Final decision and ranking
Key rationale (“Why this candidate?”)
Trade-offs acknowledged (“We chose X over Y because…”)
Concerns to address in onboarding

Why this matters: If the hire doesn’t work out, you can review what you prioritized and what you missed—improving your SkillDNA for next time.

Step 6: Generate Offer

Click Extend Offer
EffectvHire populates offer details from candidate profile
Review compensation, start date, terms
Send offer letter (integrated with DocuSign or manual)

Best Practices for Decision-Making

Start with SkillDNA, not opinions: “What did we agree matters most?”
Use evidence, not impressions: “Candidate A is better at X because…”
Acknowledge trade-offs explicitly: “We’re choosing strong technical over strong product because…”
Don’t over-optimize for “likability”: Senior leadership hires need capability first
Have a backup: Always rank #2 in case #1 negotiations fail
Document concerns: If you hire someone with a known gap, plan to address it in onboarding

AI Copilot (Optional)

Overview

The AI Copilot is an optional feature that verifies interviewer assessments for consistency and potential bias. It requires explicit candidate consent.

How It Works

Candidate consents: During interview process, candidate opts in
Interviews are recorded (audio or video, depending on format)
AI Copilot analyzes transcript:
- Extracts evidence for each skill assessed
- Generates independent ratings based on candidate responses
- Compares AI ratings to interviewer ratings
Flags inconsistencies:
- If interviewer rating differs significantly from AI analysis, flags for review
- Highlights potential bias indicators (e.g., “culture fit” used as proxy for demographic bias)

Enabling AI Copilot

Navigate to Settings → AI Copilot
Toggle Enable AI Copilot
Configure consent workflow:
- Automatically request consent after candidate agrees to interview
- OR manually request consent for specific candidates

Candidate Consent Workflow

When enabled, candidates receive:

Subject: Optional: Interview Recording & AI-Assisted Evaluation

Dear [Candidate Name],

As part of our commitment to fair and consistent hiring, we offer an optional

AI Copilot feature that helps ensure interview evaluations are objective and

evidence-based.

If you consent, your interviews will be recorded and analyzed by AI to:

- Verify that interviewer assessments are consistent with your actual responses

- Flag potential inconsistencies or bias in evaluation

- Provide you with transparency into how you were assessed

This is OPTIONAL. Your decision will not affect your candidacy.

Benefits to you:

✅ Increased objectivity in evaluation

✅ Protection against unconscious bias

✅ Transparency in assessment process

Your data:

- Recordings are encrypted and deleted within 90 days

- Only used for this hiring process

- Not shared with third parties

- You can withdraw consent anytime

[ ] Yes, I consent to interview recording and AI-assisted evaluation

[ ] No, I prefer interviews without recording

[Candidate signature]

Reviewing AI Copilot Insights

After interviews are completed:

Navigate to Candidates → [Name] → AI Copilot Report
Review consistency analysis

Example AI Copilot Report

CANDIDATE: Sarah Chen

ROLE: VP Engineering - Platform

INTERVIEWS ANALYZED: 4 of 6 (Rounds 2-5, candidate consented)

CONSISTENCY ANALYSIS:

SKILL: Technical Architecture

Interviewer Rating: 8.5/10 ✅

AI Copilot Rating: 8.7/10 ✅

Status: CONSISTENT (within 0.5 points)

Evidence Alignment:

✅ Interviewer cited: AWS expertise, 10M+ TPS, distributed systems depth

✅ AI confirmed: Candidate described AWS multi-region architecture, mentioned

specific tools (Kinesis, DynamoDB), explained CAP theorem trade-offs

✅ No red flags

---

SKILL: Engineering Leadership - People Management

Interviewer Rating: 9/10 ✅

AI Copilot Rating: 8.5/10 ⚠️

Status: SLIGHT VARIANCE (0.5 points, within acceptable range)

Evidence Alignment:

✅ Interviewer cited: Scaled team 12→45, 92% retention, strong culture

⚠️ AI noted: Candidate described scaling initiatives but provided less specific

evidence on retention strategies and performance management processes than

interviewer suggested. Rating may be slightly generous.

Recommendation: Review transcript to verify retention data was explicitly stated.

---

SKILL: Stakeholder Management

Interviewer Rating: 8/10 ✅

AI Copilot Rating: 7/10 ⚠️

Status: MODERATE VARIANCE (1.0 point - review recommended)

Evidence Alignment:

⚠️ Interviewer cited: "Board presentations, strong executive presence"

⚠️ AI analysis: Candidate mentioned board presentations once, but did not provide

detailed examples of complex stakeholder situations. Most examples focused on

peer-level cross-functional work, not executive-level influence.

Potential Bias Flag: Interviewer used phrase "executive presence" 3 times, which

can be subjective and demographic-biased. Recommend focusing on specific behavioral

evidence instead.

Recommendation: Consider if rating should be 7-7.5 based on actual evidence provided.

---

BIAS INDICATORS:

⚠️ LANGUAGE PATTERN ALERT (Round 4 - VP Product interview):

Interviewer used subjective terms 7 times: "culture fit" (3x), "executive presence"

(2x), "polish" (2x)

Research shows these terms are often unconsciously biased. Consider whether assessment

focused on behavioral evidence vs. subjective impressions.

Recommendation: Review Round 4 evaluation and ensure ratings are based on specific

examples, not general impressions.

---

OVERALL ASSESSMENT:

- 3 of 4 skills show consistent ratings (interviewer + AI aligned)

- 1 skill shows moderate variance (stakeholder management)

- 1 potential bias indicator identified (subjective language)

RECOMMENDATION: Overall strong evaluation. Consider adjusting stakeholder management

rating to 7-7.5 based on actual evidence. No major concerns.

Taking Action on AI Copilot Insights

If consistency is high (within 0.5 points):

Proceed with confidence
Interviewer assessment is well-calibrated

If moderate variance (0.5-1.5 points):

Review interview transcript
Discuss with interviewer: “AI flagged a variance—can you walk me through your thinking?”
Adjust rating if warranted

If large variance (1.5+ points) or bias flags:

Deeper review required
Consider re-interview or additional assessment
Coaching for interviewer on evidence-based evaluation

Best Practices for AI Copilot

Always make it optional: Candidates must consent
Be transparent: Explain how AI is used and why
Use as calibration tool, not replacement: Human judgment still leads
Train interviewers: Use AI insights to improve interview skills over time
Review bias flags seriously: Even if unintentional, address patterns

Integrations

Overview

EffectvHire integrates with your existing hiring stack to minimize workflow disruption.

Supported ATS Platforms

Greenhouse (Full integration)
Lever (Full integration)
Workday (Candidate sync only)
iCIMS (Candidate sync only)
BambooHR (Limited integration)

ATS Integration Setup

Navigate to Settings → Integrations → ATS
Click Connect [Your ATS]
Authorize EffectvHire access (OAuth flow)
Select permissions:
- ✅ Read candidate data
- ✅ Read job requisitions
- ✅ Write interview feedback
- ⚠️ Optional: Write candidate scores
Map fields:
- Match EffectvHire fields to your ATS custom fields
- Example: Map “SkillDNA Match %” to Greenhouse custom field “EH_Overall_Match”

What Gets Synced

From ATS → EffectvHire:

Candidate profiles and resumes
Job requisitions and descriptions
Interview schedules and participants
Existing feedback and notes

From EffectvHire → ATS:

Skill match reports (as custom field or attachment)
Interview evaluations and ratings
Decision recommendations
Hiring rationale

Interview Intelligence Tools

BrightHire Integration:

Settings → Integrations → BrightHire
Connect accounts
EffectvHire automatically pulls transcripts
Use transcripts as input to AI Copilot analysis

Metaview Integration:

Settings → Integrations → Metaview
Similar workflow to BrightHire
Transcripts used for evidence extraction

Calendar Integration

Google Calendar / Outlook:

Settings → Integrations → Calendar
Sync interview schedules
Automatically send calendar invites with interview guides attached

Document Signing

DocuSign Integration:

Settings → Integrations → DocuSign
Generate offer letters directly from EffectvHire
Track signature status

Slack/Teams Notifications

Optional: Get notified when:

New candidate completes screening
Interview evaluation submitted
All evaluations complete for a candidate
Decision required

Setup:

Settings → Integrations → Slack (or Teams)
Select notification preferences
Choose channel for notifications

Best Practices

For Hiring Managers

1. Invest Time in SkillDNA Creation

Why: A precise SkillDNA is the foundation of your entire process. Garbage in = garbage out.

How:

Involve 3-5 stakeholders in SkillDNA review
Define proficiency levels explicitly (what does 8/10 mean?)
Document trade-offs and context
Test SkillDNA on 2-3 candidates before finalizing

Time commitment: 3-4 hours upfront saves 20+ hours in debate later

2. Set Clear Expectations with Interviewers

Before interview rounds start:

15-minute group briefing: “Here’s what we’re assessing and why”
Assign specific skills to specific interviewers
Clarify: “Your job is to assess X and Y, not to make a hire/no-hire decision”

After interviews:

Review evaluations within 48 hours
Follow up on vague or missing evidence
Calibrate: “You rated this 9/10 but provided limited evidence—can you elaborate?”

3. Use Data in Debriefs, Not Just Opinions

Bad debrief:

“I really liked Candidate A”
“Candidate B felt more experienced”
“I have concerns about culture fit”

Good debrief:

“Candidate A is 9/10 on technical architecture based on X evidence”
“Candidate B has more years of experience (12 vs. 8), but Candidate A has more relevant scale experience (10M TPS vs. 1M TPS)”
“I have concerns about Candidate C’s stakeholder management—they struggled to articulate how they’d influence without authority”

Use EffectvHire’s Decision Dashboard to anchor the conversation.

4. Document Why You Hired (or Didn’t Hire)

For successful hires:

Record key strengths and trade-offs
Note any concerns to address in onboarding
Set 90-day milestones to validate decision

For candidates you declined:

Document specific gaps: “Strong technical, but limited leadership scale”
Helps refine SkillDNA for next search

For Interviewers

1. Prepare for Every Interview

15 minutes before:

Read interview guide
Review candidate’s SkillDNA match report
Note what’s already been assessed (avoid redundancy)

Don’t: Wing it or rely on generic questions

2. Assess Skills, Not “Likability”

Focus on:

Can they do the job at the required proficiency level?
What evidence supports this?

Avoid:

“I liked them” / “I didn’t vibe with them”
“Culture fit” (often code for demographic bias)
“Gut feel” without evidence

Self-check: If you can’t cite specific examples from the interview, your rating isn’t calibrated.

3. Submit Evaluations Within 24 Hours

Why: Memory fades fast. By day 3, you’re reconstructing, not remembering.

How:

Block 15 minutes immediately after interview
Jot down key evidence while fresh
Complete formal evaluation same day

4. Be Honest About Gaps

Don’t inflate ratings to “help the candidate”—it helps no one.

Don’t deflate ratings because you’re having a bad day.

Calibrate with peers: If you’re consistently the harshest or easiest grader, adjust.

For Recruiters

1. Use SkillDNA Match Reports in Sourcing

When reaching out to passive candidates:

Reference specific skills: “I see you have deep experience in distributed systems architecture…”
Not: “We have a VP Engineering role you might be interested in”

Why: Shows you’ve done homework, increases response rate.

2. Set Candidate Expectations Early

In initial phone screen:

“We use a structured interview process with EffectvHire”
“Each interviewer will assess specific skills”
“You’ll see consistency in how we evaluate—we’re looking for evidence, not just impressions”

Why: Candidates appreciate transparency and structure.

3. Leverage Decline Data

Track why candidates didn’t move forward:

Skill gaps (which skills specifically?)
Compensation misalignment
Timing issues

Use to refine sourcing: If 70% of declines are due to “insufficient leadership scale,” adjust sourcing criteria.

For Executives / Decision-Makers

1. Validate the Process, Not Just the Outcome

Ask:

“How many stakeholders reviewed the SkillDNA?”
“What was interview coverage? Did we assess every critical skill?”
“Show me the Decision Dashboard—where did candidates differ?”

Don’t just ask: “Who did you like?”

2. Hold Teams Accountable for Evidence

In decision meetings:

Push back on vague statements: “What specific evidence supports that rating?”
Insist on trade-off discussions: “If we’re choosing A over B, what are we giving up?”

Culture change: Over time, teams learn that opinions without evidence don’t fly.

3. Track Hiring Outcomes

90-day check-in:

Is the hire performing as expected?
Were our SkillDNA priorities correct?
What did we miss?

Use data to improve: If 40% of hires underperform on stakeholder management, that skill needs more assessment weight.

Troubleshooting

Common Issues and Solutions

Issue: AI-Generated SkillDNA Doesn’t Match Our Needs

Symptoms:

Skills are too generic or too specific
Missing critical competencies
Wrong proficiency levels

Solutions:

Customize heavily: AI extraction is a starting point, not the final answer
Add context: Use the “Context” field to explain why certain skills matter
Iterate with stakeholders: Get input from 3-5 people who know the role
Use templates: If you’ve hired similar roles before, start from a saved template

Issue: Candidates Aren’t Matching Well in Screening

Symptoms:

All candidates show <60% match
SkillDNA matching seems inaccurate

Solutions:

Check SkillDNA specificity: Are skills too narrow? (e.g., “Must have 5+ years React” vs. “Strong frontend engineering”)
Review proficiency levels: Are you requiring 9/10 for skills that could be 7/10?
Source differently: If no candidates match, your talent pool may be wrong
Consider SkillDNA revision: Market reality may require adjusting expectations

Issue: Interviewers Aren’t Using Interview Guides

Symptoms:

Interviewers ignore recommended questions
Evaluations submitted without evidence
Coverage gaps in assessments

Solutions:

Brief interviewers synchronously: 15-min meeting to explain the “why”
Make guides shorter: If guides are 5+ pages, condense to key bullets
Show value: Share example of how guides improved past hires
Hold accountable: Hiring manager reviews evaluations and pushes back on vague submissions

Issue: Evaluations Show Huge Variance Between Interviewers

Symptoms:

Interviewer A rates candidate 9/10, Interviewer B rates same skill 5/10
Decision debrief is contentious

Solutions:

Review rubrics together: Are people interpreting proficiency levels differently?
Calibrate with transcripts: If AI Copilot is enabled, check which rating is more evidence-based
Discuss in debrief: “Jamie, you rated technical 9/10. Alex, you rated 5/10. Walk me through your evidence.”
Re-interview if necessary: If variance is >3 points on a critical skill, consider additional assessment

Issue: Decision Dashboard Isn’t Loading or Shows Errors

Symptoms:

Dashboard blank or spinning
“Error loading candidate data”

Solutions:

Check interview completion: Ensure all interviewers have submitted evaluations
Verify permissions: Ensure you have “Hiring Manager” or “Admin” role
Clear cache: Refresh browser, clear cookies
Contact support: If issue persists, support@effectv.ai

Issue: ATS Integration Isn’t Syncing

Symptoms:

Candidates not appearing in EffectvHire
Evaluations not writing back to ATS

Solutions:

Re-authorize integration: Settings → Integrations → Reconnect ATS
Check field mapping: Ensure custom fields are correctly mapped
Verify permissions: ATS admin may need to grant additional OAuth scopes
Manual sync: Click “Sync Now” in Integrations dashboard
Contact support: Provide ATS name, company size, specific error messages

Issue: Candidates Declining Due to Process Length

Symptoms:

“Your process is too long” feedback
Drop-offs after Round 3-4

Solutions:

Compress rounds: Can you combine two 60-min rounds into one 90-min?
Explain the why: “We use a structured process to ensure fairness and reduce bias”
Offer flexibility: Virtual interviews, flexible scheduling
Fast-track top candidates: If someone is clearly exceptional after 3 rounds, accelerate decision

Issue: AI Copilot Flags Bias but We Disagree

Symptoms:

AI flags “culture fit” language as biased
Interviewer believes assessment was fair

Solutions:

Review transcript: Is the concern valid based on actual conversation?
Discuss with team: “AI flagged this—let’s discuss whether we should adjust”
Provide feedback: If AI is consistently wrong, report to EffectvHire support
Remember: AI is a tool, not a judge. Human decision is final.

Getting Help

Documentation: https://docs.effectv.ai

Email Support: support@effectv.ai (response within 24 hours)

Live Chat: Available in-app (bottom right corner)

Training Resources:

Video tutorials: https://effectv.ai/tutorials
Webinars: Monthly “Office Hours” sessions
Best practices library: https://effectv.ai/best-practices

Account Management:

Dedicated support for annual subscribers
Quarterly business reviews
Custom training for teams of 20+

Appendix: Sample Workflows

Workflow 1: Hiring a VP Engineering

Week 0: Preparation

Create SkillDNA (3 hours)
Finalize with 5 stakeholders (2 hours)
Import 20 candidates from ATS

Week 1: Screening

AI screening completes (automated)
Hiring manager reviews top 8 candidates (2 hours)
Shortlist 4 candidates for interviews

Weeks 2-4: Interviews

Each candidate: 5 rounds over 2 weeks
- Phone screen (recruiter, 30 min)
- Technical deep dive (engineering manager, 90 min)
- Leadership round (current VP, 60 min)
- Cross-functional (VP Product, 60 min)
- Executive round (CTO, 45 min)

Week 5: Decision

All evaluations submitted
Decision debrief (60 min)
Extend offer to #1 choice
Backup offer ready for #2 if needed

Total Time: 5 weeks Decision Quality: High confidence, evidence-based

Workflow 2: Hiring a Director of Product Management

Week 0: Preparation

Create SkillDNA from template (1 hour)
Customize for specific product area (1 hour)
Import 15 candidates

Week 1: Screening

AI screening (automated)
Review top 6 candidates (1 hour)
Shortlist 3 for interviews

Weeks 2-3: Interviews

Each candidate: 4 rounds over 10 days
- Phone screen (30 min)
- Product case study (90 min)
- Leadership & strategy (60 min)
- Team & culture (45 min)

Week 4: Decision

Decision debrief (45 min)
Extend offer

Total Time: 4 weeks

Workflow 3: Hiring a Principal Engineer

Week 0: Preparation

Create SkillDNA (2 hours)
Focus on technical depth + system design
Import 12 candidates

Week 1: Screening

AI screening (automated)
Technical hiring manager reviews top 5 (1.5 hours)
Shortlist 3 for interviews

Weeks 2-3: Interviews

Each candidate: 4 rounds
- Phone screen (30 min)
- System design (2 hours)
- Code review & architecture (90 min)
- Team & values (45 min)

Week 4: Decision

Debrief (45 min)
Offer

Total Time: 4 weeks

Glossary of Terms

Assessment Rubric: Specific criteria for evaluating a candidate’s proficiency in each skill dimension, typically on a 1-10 scale with defined behavioral indicators.

Coverage Dashboard: Real-time view showing which skills have been assessed across interview rounds and which remain uncovered.

Decision Dashboard: Side-by-side candidate comparison tool showing skill ratings, evidence, and trade-offs to facilitate structured hiring decisions.

Interview Guide: Customized document for each interviewer containing candidate-specific questions, assessment rubrics, and context for the skills being evaluated.

Interview Plan: Strategic distribution of skill assessments across multiple interview rounds, ensuring complete coverage without redundancy.

Proficiency Level: A 1-10 scale indicating required mastery for each skill, with explicit definitions of what different levels mean in your organizational context.

SkillDNA: The precise, multi-dimensional skill framework that defines success criteria for a specific role, including technical skills, leadership capabilities, and domain expertise.

Skill Bucket: A category of related competencies that together represent a key dimension of job performance.

Skill Match Report: AI-generated analysis showing how well a candidate’s resume and experience align with the role’s SkillDNA, including skill-by-skill scoring and gap analysis.

Version History

v1.0 (Current)

Initial product documentation
Core workflows: SkillDNA creation, screening, interview planning, decision-making
AI Copilot (optional feature)
ATS integrations
Best practices and troubleshooting

Feedback and Improvements

This documentation is a living resource. We continuously improve based on user feedback.

Have suggestions? Email docs@effectv.ai

Found an error? Click “Report Issue” (bottom right of any page)

Want to contribute? We welcome customer-contributed best practices and case studies

Thank you for using EffectvHire. We’re committed to helping you make better senior leadership hiring decisions.

For additional support: support@effectv.ai | https://www.effectv.ai

EffectvHire Product Documentation

Decision Intelligence for High-Stakes Hiring

Table of Contents

Getting Started

Welcome to EffectvHire

Key Terminology

Creating Your SkillDNA

Overview

Screening Candidates

Step 1: Import Candidates

Planning Interviews

Overview

Conducting Interviews

Overview

Real-Time Coverage Tracking

Version History

Feedback and Improvements