Magnetic Energy HR & Staffing Operating Plan

Status: Detailed management planner. This document updates the plan and does not authorize execution.

Human development lead: Walt, coordinating with management and AI development agents.

Planning method: Dependencies, acceptance evidence and approvals — not arbitrary dates.

Executive Summary

Magnetic Energy is connecting recruiting, screening, hiring, provisioning, training, measurement, compensation and offboarding into one auditable staffing operating system.

This reconciliation does not change the recruiting, WhatsApp Hiring Manager, application or screening plans. It updates only the systems now built and the decisions specified in this revision: actual setter onboarding, explicit categories, training, Magnetic Analytics, Supervisor compensation, payroll-time evidence and a future Payroll module.

Current Workforce and Target

Management and Change Control

• Named-person onboarding and offboarding require Walt's explicit manual trigger.
• Setter category changes require a documented human decision; categories are never inferred.
• Payroll periods require reviewed calculations and human approval until separately approved payout automation exists.
• No task becomes ready because a date arrives. Dependencies, tests, ownership, evidence and approval control readiness.

Zero-Budget Global Recruiting Strategy

Since there is no budget for recruitment ads, we will post to every free channel available worldwide. The application form on MagneticDash will be the single entry point for all candidates. All positions are remote, so we recruit globally — not just the US.

Free Job Posting Channels (Global)

Application Form (Single Entry Point)

• GHL Forms embedded on a MagneticDash /careers page (public-facing)
• Separate form per role (closer, call center, canvasser, QA) with role-specific screening questions
• All forms feed into the same candidates table in Supabase with role field
• Auto-reply via GHL SMS/email confirming application received and next steps

WhatsApp AI "Hiring Manager" — Pre-Screening Chat (New Layer Before Form)

Why WhatsApp?

• Global dominance: WhatsApp has 2+ billion users across 180+ countries. In our target recruiting markets — the Philippines, India, Latin America, Africa, Eastern Europe — WhatsApp IS the primary communication tool. Many candidates use it more than email or phone calls.
• US adoption is growing: Even in the US, WhatsApp usage is climbing rapidly, especially among younger demographics and immigrant communities — exactly the talent pools we want to reach.
• Lower friction than a web form: Job seekers are far more likely to send a WhatsApp message than navigate to a website and fill out a form. They already have the app. They're already comfortable chatting. No new account, no new website, no friction.
• Instant, 24/7/365: The AI Hiring Manager responds instantly regardless of time zone. A prospect in Manila messaging at 3 AM their time gets the same quality engagement as one in Chicago at noon.
• Conversational = higher conversion: People commit to things through conversation, not forms. The WhatsApp chat turns a cold job posting into a warm, personalized recruitment conversation.

How the Flow Works

What the AI Hiring Manager Discusses (Friction Removal)

The AI is trained on everything a prospect might want to know before committing to apply. It proactively addresses and answers questions about every common point of friction:

Pre-Screening Criteria (Embedded in Conversation)

The AI doesn't just answer questions — it actively evaluates every prospect during the natural flow of conversation. By the time the chat concludes, we have structured data on:

Example Conversation

Design Principles

• Warm and human: The AI is genuinely enthusiastic, professional, and caring. It never feels like a form or a bot — it feels like a conversation with a hiring manager who wants you on the team.
• Adaptive: It reads what the prospect cares about most. If they're worried about pay, it dives deep on compensation. If they're nervous about training, it reassures them about the AI roleplay system. It follows the prospect's lead.
• Creates urgency and excitement: "We're actively hiring right now," "You'd be a great fit for our team," "I'd love to get your application in front of the team today." It sells the opportunity.
• Handles objections naturally: "I've never done sales" → "That's what our training is for." "I'm not sure about the pay" → breaks down the earning math with a concrete example. "I need to think about it" → "Totally understand — what questions can I answer to help?"
• Always drives toward conversion: Every conversation has one goal — getting the prospect informed, excited, and through the application form link. The AI is a closer.
• Maintains the illusion: At no point does the AI reveal it is automated. It presents as a human hiring manager with a name, personality, and genuine investment in the prospect's success.

Technical Implementation

• Platform: Meta WhatsApp Business API (via Meta Business Platform) connected to an AI agent powered by Hermes/OpenRouter
• Orchestration: N8N workflow — incoming WhatsApp message webhook → AI generates contextual response using conversation history → sends reply via WhatsApp Business API
• Conversation storage: All conversations stored in Supabase (whatsapp_conversations table) with prospect phone number, full message history, timestamps, and AI assessment scores
• Pre-screening data: Structured assessment results (English score, tech readiness, motivation level, role fit) stored alongside the conversation and passed to the candidates table when the prospect advances
• Form handoff: When the AI determines a prospect is pre-qualified, it generates a personalized GHL form link with their WhatsApp data (name, phone, role interest) pre-populated — reducing form fields they need to fill
• Knowledge base: The AI is trained on a comprehensive knowledge document covering all company details, job descriptions, pay scales, training process, requirements, and common FAQs — updated as our offerings evolve
• Human escalation: If the AI encounters a question it can't answer or a prospect specifically requests to speak with a human, it flags the conversation for Walt to take over manually

Connected HR Operating Architecture

AI and Automation Strategy

AI Application Screening

• N8N receives application → sends resume + answers to AI (Hermes via API or OpenRouter)
• AI scores application on role-specific criteria (experience, answers quality, communication skills)
• Applications scoring below threshold are auto-rejected with polite GHL SMS/email
• Qualified applications advance to interview stage automatically
• Prompt engineering: Role-specific scoring rubrics stored in N8N

Training and Certification

• The `/agc-next` training package, 30-question certification quiz, instructor key, quick-reference guide, sandbox exercises and instructor materials are built.
• Current written passing requirement and live demonstration remain part of certification.
• AI roleplay and full training-record automation remain planned where not implemented.
• Weekly Roam On-Air training adds continuous human-led reinforcement.

Analytics-Assisted Management

• All active campaigns are dynamically covered by the approved read-only collector.
• Specialist, Supervisor, Manager and Unclassified categories remain explicit; Administration & Management is separate.
• Payroll time, execution quality, outcomes and Quality Grades stay distinct.
• A future Recruits / Onboards / Trainees view will connect pipeline, certification and early-performance evidence; it is planned, not built by this update.

Detailed Workstream Planner

payroll_talk_time = SUM(wait_sec) + SUM(talk_sec)

Data and Integration Planner

Workstream Readiness Gates — No Timeline

A workstream may progress when its prerequisites are met. Elapsed time never waives a gate.

Success Measures

• Staffing reporting distinguishes applicants, screened candidates, hired, provisioned, certified, active and metric-qualified Supervisors.
• Manual WhatsApp, Tik Tik and MagneticDash steps remain visible until truly automated.
• Initial certification and weekly training evidence are visible by staff member.
• Personnel recommendations include denominator, evidence period, quality, outcomes, training and human rationale.
• Every proposed payout reproduces from a versioned rule and immutable evidence.

Management Decision Register

Immediate Planning Actions — Not Execution

1. Approve this reconciled operating framework.
2. Create separately scoped implementation plans per workstream with owners, dependencies, tests, approval gates and rollback.
3. Define Supervisor qualification thresholds and evidence window.
4. Define Roam On-Air operating policy and pilot criteria without creating the event series.
5. Design the Recruits / Onboards / Trainees Analytics view and decision-packet schema without deployment.
6. Define Payroll rule/version/ledger contracts and reconcile them to the Supervisor workbook without connecting payment rails.
7. Keep the recruiting, WhatsApp, application and screening plans unchanged until management intentionally revises them.

AI Audio/Video Screening Pipeline (Critical Feature)

Overview

Every applicant submits a role-specific recording as part of their application. AI grades the recording AND the screening quiz before any human sees the application. Only candidates who pass both the AI recording grade and the AI quiz grade are offered an interview.

Application Recording Requirements

How It Works (Technical Flow)

[Applicant fills GHL Form]
         │
         ▼
[Recording prompt appears] — "Record yourself reading this script..."
         │
         ▼
[Applicant submits audio/video via form]
         │
         ▼
[Screening quiz (5-10 questions)]
         │
         ▼
[N8N receives submission] ──► [AI Processing]
         │                         │
         │                    ┌────┴────┐
         │                    ▼         ▼
         │              [Whisper      [AI grades
         │               transcribe    quiz answers]
         │               audio/video]
         │                    │         │
         │                    ▼         ▼
         │              [AI grades recording:
         │               tone, clarity,
         │               confidence,
         │               objection handling,
         │               script adherence]
         │                    │
         │                    ▼
         │              [Composite score:
         │               recording_weight + quiz_weight]
         │                    │
         ▼                    ▼
[Candidate in DB] ◄── [Score stored in candidates.ai_screening_score]
                           │
                     ┌─────┴─────┐
                     ▼           ▼
                [Score >= 70]  [Score < 70]
                     │           │
                     ▼           ▼
              [Tag: AI Passed] [Auto-reject via
              [Notify Walt     GHL SMS/email]
               or Michael]
                     │
                     ▼
              [Interview offered]

AI Grading Rubric (Call Center Agent Example)

AI Grading Rubric (Closer Video Example)

AI Grading Rubric (Canvasser Video Example)

Technical Implementation

• Recording capture: GHL Form doesn't support file uploads natively. Options:
• Option A (Recommended): MagneticDash /apply page with built-in audio/video recorder (browser MediaRecorder API) → upload to Supabase Storage → link stored in GHL custom field
• Option B: Third-party embed (Vocal Video, VideoAsk) → webhook to N8N
• Transcription: Whisper API (existing pipeline) for audio. For video, extract audio track first (ffmpeg) then Whisper.
• AI grading: N8N sends transcription + rubric to AI (OpenRouter or Hermes) → receives structured score JSON → stores in Supabase
• Rejection/acceptance: N8N triggers GHL SMS based on score threshold (70 for call center, 75 for closers)
• Storage: Recordings stored in Supabase Storage bucket applicant_recordings (retained for 90 days post-decision)

Recording Prompt Examples

Call Center Agent (Audio):

"You are calling a homeowner. Start with a strong icebreaker to get them talking. Then: confirm their information, handle their objections confidently, request any additional details needed, and close the call by either transitioning to a live transfer or booking a firm appointment. You will be evaluated on your English fluency, confidence, tone, pacing, objection handling, and your ability to close."

[Homeowner scenario provided on screen]

Objection 1: "I didn't sign up for anything."

Objection 2: "I already have solar."

Objection 3: "I need to talk to my spouse first."

QA (Audio — Multiple Assessments):

"This assessment has three parts. Part 1: Act as an assistant following up to confirm a homeowner's information and appointment — confirm their details and guide them toward a confirmed appointment. Part 2: You are receiving a live call transfer from a call center agent — conduct an assessment, perform additional qualification confirmations, then either pass the homeowner to a sales closer or book the appointment yourself. Part 3: Listen to the following sample call recording and identify what went well, what could be improved, and any compliance issues."

[Homeowner scenarios and sample call provided on screen]

Closer (Video):

"Record a 60-90 second video of yourself pitching solar to a homeowner. Pretend you are sitting at their kitchen table after they invited you in. Include: who you are, why solar makes sense for their home, and how you'd handle the objection 'I can't afford the monthly payment.'"