Status: Active implementation plan. This revision authorizes planning and source-controlled plan publication; GHL, n8n, recruiting-channel, and candidate-facing mutations still require their own verified execution step and readback.
Human development lead: Walt, coordinating with management and AI development agents.
Planning method: Dependencies, acceptance evidence and approvals — not arbitrary dates.
Magnetic Energy is connecting recruiting, screening, hiring, provisioning, training, measurement, compensation and offboarding into one auditable staffing operating system.
This revision preserves the original WhatsApp-led recruiting design while adopting the approved GHL, Cloudflare and Supabase architecture: job postings drive prospects first to the WhatsApp Business “Hiring Manager” for conversation and pre-screening; qualified prospects then receive the role-specific GHL application. GHL remains the candidate CRM and native workflow authority; Supabase is the structured HR operations/evidence database for job postings, publication history, WhatsApp conversations, screening evidence and funnel events; GitHub-backed Cloudflare Pages/Workers supply public pages and custom applicant UX; Cloudflare R2 holds approved applicant media; n8n handles conversational AI, screening, training, analysis and cross-system orchestration. The setter lane launches first, with the closer lifecycle mirrored by reference.
The production dashboard contains an extensive Call Center Analytics buildout covering setter activity, outcomes, Call Intelligence, Quality Grades, category-safe reporting, time evidence and related management views. Analytics supports evidence-based progression, coaching, remediation, category review and offboarding; authorized humans make personnel decisions.
Current release state: Production at the canonical URL below. Setter reporting is materially built; recruiting/onboarding views and the separate closer section advance as explicit workstreams.
The target is not 30 accounts. It is 30 trained, certified, appropriately equipped, Analytics-visible Supervisor setters who meet the approved metrics.
| Workforce | Current | Target | Planning note |
|---|---|---|---|
| Closers | 3 | Original target 6 | Mirrored recruiting, onboarding and dedicated Analytics lane included by reference |
| Setter agents — Supervisor | 2 | 30 metric-qualified | Primary staffing objective |
| Specialist / Manager setters | None stated in this revision | Not separately set | Explicit categories; criteria/compensation require decisions |
| QA / Support | 0 original baseline | 1 original baseline | Original role plan unchanged |
| Canvassers / Setters | 0 original baseline | 4 original baseline | Original role plan unchanged |
Since there is no budget for recruitment ads, we will post to every appropriate free channel available worldwide. The primary call to action is “Message our Hiring Manager on WhatsApp”, carrying a posting-specific source code into the conversation. Pre-qualified prospects receive the role-specific GHL application; a direct GHL link remains an accessibility and outage fallback. All positions are remote, so we recruit globally — not just the US.
| Channel | Cost | URL / Method |
|---|---|---|
| Indeed | Free basic posting | indeed.com/hire (global — 60+ countries) |
| Free basic posting | linkedin.com/hiring (global reach) | |
| ZipRecruiter | Free basic posting | ziprecruiter.com/hire |
| Facebook Groups | Free | Digital nomad groups, remote work groups, solar industry groups, work-from-home communities — global reach |
| Craigslist | Free (some markets) | craigslist.org (select markets charge $10-25) |
| Nextdoor | Free local posting | nextdoor.com for local canvasser recruitment |
| Google for Jobs | Free (via schema markup) | GitHub-backed Cloudflare careers page with schema.org JobPosting markup |
| Solar Industry Job Boards | Free | SolarPowerWorld, SolarEnergyJobs (some free options) |
| Veterans Job Boards | Free | Hiring Our Heroes, RecruitMilitary (Veterans focus aligns with company values) |
| College Job Boards | Free | Local college career centers (canvasser/entry-level roles) |
| Roam Community | Free | Post in Roam groups (existing team referrals) |
| Employee Referral | $0 (bonus only on hire) | Track source/referrer in GHL; apply only approved eligibility rules |
| Remote-Specific Job Boards | Free | WeWorkRemotely, Remote.co, RemoteOK, Working Nomads — ideal for call center agents and closers |
| Digital Nomad Communities | Free | Facebook digital nomad groups, NomadList forums — candidates with native English, self-motivated, timezone-flexible |
| Upwork / Fiverr | Free listing | Post contract-to-hire positions — screen for full-time conversion |
| Free | r/forhire, r/remotejobs, r/solar, r/workonline — targeted subreddits for each role |
Before a prospect ever reaches the GHL application form, they interact with our AI-powered WhatsApp Business chat. This chat is presented to prospects as our "Hiring Manager" — a real person who answers their questions, builds excitement about joining Magnetic Energy, and screens for obvious mismatches. Prospects do not know they are speaking with an AI. By the time they reach the formal application form, they are informed, pre-qualified, and genuinely motivated to complete it.
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:
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:
WHY THIS IS A GAME-CHANGER: Every incomplete application is a lost hire. Traditional job posting → web form funnels have 70-80% abandonment rates. The WhatsApp AI Hiring Manager transforms this funnel — by the time a prospect reaches our application form, they have had a personalized conversation, all their questions answered, all their concerns addressed, and their excitement built. They are pre-qualified. They are motivated. Getting them to complete the form IS making a sale — and the WhatsApp chat is our best salesperson, working 24/7/365, scaling to thousands of simultaneous conversations at zero additional cost.
Without talented staff, we cannot succeed. This layer ensures we don't lose great candidates to friction, confusion, or unanswered questions — the three things that kill applications.
| System | HR responsibility | State / rule |
|---|---|---|
| WhatsApp Business | Primary first-contact funnel, job questions, conversational qualification, objection handling and warm handoff to the formal application | First funnel layer |
| GHL | Authoritative candidate/contact CRM, forms, recruitment pipeline, communications summary, tasks, calendars, interviewer feedback, offer/onboarding status and native workflows; preferred host for a dedicated workflow-scoped Voice AI homeowner simulation after tenant verification | Primary workflow platform |
| Supabase PostgreSQL | Recruitment operations/evidence: postings and variants, channel publication ledger, WhatsApp messages, questionnaires, systems checks, voice samples, AI role-play sessions/transcripts/scorecards, screening evidence and funnel events; candidate rows link to immutable GHL contact ID | Required HR database |
| Cloudflare Pages/Workers + GitHub | Version-controlled careers pages, custom applicant UX, technical checks, signed webhooks/uploads and role-specific portals | Native Git deployments only |
| Cloudflare R2 | Approved applicant audio/video and other binary evidence under a defined retention/deletion policy | Media store |
| n8n | WhatsApp conversational AI, recruit screening, transcription, evidence-cited voice/role-play scoring, training analysis and cross-system orchestration from signed events | Extension, not system of record |
ViciDial /agc-next | Setter user/phone/workspace, calls, recordings and authoritative call evidence after hire | Named onboarding built |
| Production Magnetic Analytics | Extensive setter/call-center analytics now; recruiting/onboarding views and a separate closer section by explicit build | Production |
| Roam / Tik Tik | Approved team communications, training and manual membership where applicable | Mixed / manual |
| Payroll module | Versioned proposed payout ledger and human approvals; no direct payout authority | Planned |
job_posting_publications: channel, market, external URL/post ID, source code, variant, state, published/expiry dates, last verification and owner.| Setter screening gate | Evidence | Progression rule |
|---|---|---|
| Short questionnaire | Role understanding, availability, experience, consent and objective requirements | Complete and internally consistent, or human clarification |
| Systems readiness | Hardware/software/OS/browser/headset/microphone/network/tool results | Approved minimums pass; remediable failures may retry |
| Voice sample | Recording, transcript and job-relevant communication dimensions | Evidence-cited analysis; no protected-trait inference or AI-only rejection |
| AI homeowner role-play | Versioned scenario, full call, transcript, objections, script behavior and defined outcome | Versioned scorecard reaches human review |
| Human decision | Complete packet plus reviewer reason | Interview, retry, hold or rejection |
Primary flow: tracked job post → WhatsApp Hiring Manager → Supabase conversation/evidence → pre-qualified handoff → GHL form/questionnaire → systems check → voice sample → live GHL AI homeowner role-play → n8n evidence-cited scorecard → human review → GHL calendar/interview.
| Item | Current reality | Completion evidence |
|---|---|---|
| Identity + category | Explicitly validated; never inferred | Approved named record |
| GHL user | Approved setter permission template; assigned-data-only | User ID, permissions, login |
| ViciDial user + phone | Controlled named-agent creation | User, phone, level and sign-in |
| Analytics access | Separate own-agent-only credential | Own data works; cross-agent denied |
| Roam visitor badge | Badge plus selected approved groups | Badge and exact groups verified |
| WhatsApp group | Manual admin add | Admin confirmation |
| Tik Tik | Staff downloads; admin shares group | Install and group access |
| GHL / Cloudflare HR content | Candidate record and onboarding content are role-scoped | GHL stage, content access and repository deployment verified |
| GHL follower branch | Dedicated named-setter workflow branch | Routing verified |
| Welcome | Email + approved team announcement | Delivery evidence |
Sequence: validate → snapshot/preflight → GHL → ViciDial/phone → production Analytics → Roam → WhatsApp → Tik Tik → role-scoped GHL/Cloudflare content → workflow → welcome → end-to-end verification.
Cadence: weekly and ongoing. Management chooses the standing day/time and authorized host. This page does not create the event series.
| Evidence | Source | Use |
|---|---|---|
| Event/host/start/end | Roam On-Air | Training record |
| RSVP + join duration | Roam attendance | Participation only—not competence |
| Module/version | Training record | Standard taught |
| Quiz/demo | Instructor/system | Certification/remediation |
| Coaching action | HR case | Owner and closure |
| Post-training window | Analytics | Observed change with denominator |
Production source: https://magnetic-ads-dashboard.pages.dev/. Its extensive Call Center Analytics section is the setter evidence baseline. Recruiting/onboarding views are additive and do not weaken existing privacy, category or human-decision controls.
| Decision | Analytics role | Human control |
|---|---|---|
| Onboarding progression | Readiness and early evidence when pipeline view exists | Walt approves named person/stage |
| Specialist → Supervisor | Sustained approved metrics, quality, certification and reliability | Management approves category + compensation |
| Supervisor → Manager | Manager/QA capability evidence | Management approves role, level + compensation |
| Remediation | Evidence-backed gap and trend | Manager defines plan/window |
| Offboarding | Complete packet; never one metric | Explicit named trigger |
Guardrails: zero-call active users remain visible; inactive users are excluded; categories are explicit; payroll time does not substitute for quality; detailed calls/coaching remain private; missing data and denominators remain visible.
Inclusion by reference: Every lifecycle control defined for setters—source attribution, dedupe, screening evidence, human decisions, onboarding proof, training records, access control, coaching, remediation and offboarding—also applies to closers. The implementation is mirrored in control, not copied blindly: closer tools, permissions, rubrics, compensation and denominators remain role-specific.
| Lane | Closer implementation | Acceptance evidence |
|---|---|---|
| Recruiting | Closer-specific GHL form; 60–90 second video pitch/objection exercise through Cloudflare/R2 when required; Michael review route | Role/source/dedupe, media reference, rubric evidence and human decision |
| Onboarding | GHL closer user/permissions, assigned pipeline access, calendar + Google Meet readiness, approved communication groups and role content | Least-privilege login, test opportunity, meeting, follow-up and access-denial checks |
| Training | Closer script, discovery/presentation, objection handling, follow-up, pipeline hygiene, compliance and roleplay/call review | Module/version, attendance, quiz/demo, coach and closure |
| Analytics | Dedicated closer section separate from setter analytics: assigned opportunities, speed-to-contact, follow-up completion, meeting attendance, discovery/presentation progression, close outcomes, cycle time, quality/coaching and approved compensation evidence | Role-scoped denominators, source/date filters, drilldown privacy and reconciliation to GHL |
| Lifecycle | Human-approved progression, remediation, compensation review and named offboarding | Decision packet and post-change access verification |
| Category | Meaning | Compensation authority | Default level |
|---|---|---|---|
| Specialist | Entry / commission-only setter | Separate structure TBD | 1 |
| Supervisor | Standard setter | Current Supervisor workbook | 1 |
| Manager | QA/override setter duties | Separate structure TBD | 2 |
| Unclassified | Awaiting explicit classification | No assumptions | Never inferred |
Category-specific workbook with payroll talk/ready compensation, appointment/live-transfer incentives, close-timing incentives, weekly close bonus, team-bonus estimate and editable scenarios.
payroll_talk_time = SUM(wait_sec) + SUM(talk_sec)Includes all ViciDial waiting plus every bridge talk_sec—human, voicemail, dead air/open line, busy/disconnected and unanswered. Excludes pause and disposition/wrap.
| Supabase record | Purpose |
|---|---|
job_postings | Canonical role posting, approved content/version, owner, markets, status and lifecycle dates |
job_posting_variants | Channel/market copy variants and version-to-canonical relationship |
job_posting_publications | Channel, external post ID/URL, source code, publication/expiry dates, state, last verification, owner and evidence |
recruitment_sources | Stable source/referrer taxonomy and WhatsApp deep-link attribution |
candidate_links | Minimal join projection: recruitment conversation UUID/phone to immutable GHL contact ID; not a duplicate CRM |
whatsapp_conversations / whatsapp_messages | Full message history, consent/opt-out state, role/source lineage, timestamps and human escalation |
candidate_questionnaires / systems_check_runs / voice_sample_runs | Versioned answers, objective minimum checks, retries, recording/transcript references and job-relevant voice evidence |
ai_roleplay_sessions / ai_roleplay_turns / ai_roleplay_scorecards | GHL agent/workflow, scenario/rubric versions, complete conversation, objections, defined outcome, evidence-cited dimension scores, confidence and flags |
screening_runs / screening_evidence | Cross-gate evidence packet, recommendation, confidence, human disposition/reason and immutable source references |
recruitment_funnel_events | Post viewed/contacted, WhatsApp engaged, form offered/submitted, interview, hire, training, certification and active identity events |
onboarding_tasks / onboarding_forms | Role-specific onboarding requirements, secure-record references, completion/verification evidence |
training_modules / training_completions | Versioned training content, attempts, evidence, human review and certification state |
staff_system_access | External IDs, roles/scopes, state and provision/verify/revoke evidence |
staff_category_history | From/to category, evidence packet, approver and effective date |
training_events / training_attendance | Roam event/module/version and RSVP/join/completion evidence |
hr_decision_packets | Evidence range, metrics, recommendation and human decision/reason |
compensation_policies / payroll_periods / payroll_ledger | Versioned rules, immutable evidence, approvals, adjustments, locks, exports and payout trace |
A workstream may progress when its prerequisites are met. Elapsed time never waives a gate.
| Decision | State |
|---|---|
| Setter objective | Confirmed: 30 metric-qualified Supervisors |
| Current setters | Confirmed: 2 Supervisors |
| Current closers | Confirmed: 3 |
| Specialist / Manager compensation | Decision required |
| Supervisor qualification thresholds/evidence window | Decision required |
| Roam On-Air host/day/time and recording/attendance policy | Decision required before event creation |
| Analytics production release | Exact separate approval required |
| Payroll rules and approvers | Decision required |
| Direct payout automation | Future separate approval |
| Worker jurisdictions/classification | Legal/management decision required |
Today is complete only when one tracked test prospect reaches WhatsApp, GHL questionnaire, minimum systems check, required voice sample, isolated live AI homeowner role-play, evidence-cited scorecard and authorized human review without touching unrelated contacts or real homeowner/appointment data.
| Order | Do today | Owner / system | Done when |
|---|---|---|---|
| 1 | Freeze the setter role card, approved call script, short questionnaire, objective systems minimums, voice prompt, AI-homeowner scenario bank, objections, positive-outcome definitions, scoring rubric, retry policy, disclosure/consent and human escalation rules. Omit unapproved terms. | Walt • role/screening contract | One approved versioned package exists |
| 2 | Read back existing GHL, WhatsApp/Meta, Supabase, n8n, phone-number and Voice AI assets before creating anything. Verify ownership, Voice AI capabilities, recording/transcript export, workflow isolation and safe test scope. | System audit | Inventory, owners, exact capabilities and gap list are saved |
| 3 | Create/verify the Supabase recruitment ledger, adding candidate questionnaires, systems checks, voice samples, AI role-play sessions/turns/scorecards, screening evidence and funnel events. Apply RLS, least privilege, retention, audit fields and idempotency. | Supabase | Schema and access tests pass; no public privileged key exists |
| 4 | Configure the WhatsApp Hiring Manager as the primary funnel. Every post/deep link carries a source code; the assistant uses approved knowledge, records conversation/evidence, escalates uncertainty and hands qualified prospects to GHL. | WhatsApp + n8n | Test conversation is attributable, persisted and human-escalatable |
| 5 | Establish the GHL Staff-candidate contract and build the prefilled role form/workflow with questionnaire, consent, acknowledgement, evidence links, screening state, tasks, reminders and human-review routing. | GHL | One prefilled submission creates one candidate and workflow run |
| 6 | Implement the minimum systems check for approved hardware, software, OS/browser, headset/microphone, network and required tools. Return exact failures and remediation/retry path. | Cloudflare Worker/Page + GHL | Pass and controlled-fail tests produce correct evidence |
| 7 | Implement the required setter voice sample and analysis for job-relevant intelligibility, clarity, cadence, tone/tonality, vocal strength/confidence and audio quality. Do not infer protected traits or auto-reject from accent/voice analysis. | Cloudflare/R2 + n8n | Recording, transcript, evidence and review state persist |
| 8 | Build the final live AI homeowner role-play. Prefer a dedicated non-primary, workflow-scoped GHL Voice AI agent after capability readback. The candidate pitches, follows the script, handles versioned objections and seeks the approved positive next step; the AI provides no coaching and creates no real appointment. | GHL Voice AI | Only the approved test candidate can enter; unrelated contacts and real customer/calendar data are unreachable |
| 9 | Publish idempotent n8n analysis: persist call/transcript plus scenario/rubric versions; score evidence-cited dimensions and outcome; write Supabase evidence and GHL summaries/tasks; send every rejection recommendation and uncertain/low-confidence result to a human. | n8n | Replay is idempotent and no AI-only permanent rejection occurs |
| 10 | Create the GitHub-backed Cloudflare /careers/setter and screening UX with approved copy, tracked WhatsApp CTA, direct-GHL accessibility fallback, privacy notice and recording/automated-analysis disclosure. | GitHub + Cloudflare Pages | Production UX loads and attribution survives every handoff |
| 11 | Run one approved internal canary across tracked post → WhatsApp → Supabase → GHL questionnaire → systems check → voice sample → live AI homeowner role-play → scorecard → human review → calendar. Verify consent, isolation, no real appointment/customer mutation, dedupe, attribution, retries, permissions, audit and cleanup. | End-to-end | Acceptance checklist and exact readbacks pass |
| 12 | Publish one approved setter post plus channel variants only after the canary passes. Make WhatsApp the primary CTA and log every external URL/post ID, source code, state and evidence. | Recruiting + Supabase | Live post URLs and source codes reconcile to the database |
| 13 | Monitor WhatsApp and every screening gate continuously. Give every candidate an owner, complete evidence state and next action; pause added volume if any gate drops, duplicates or misroutes candidates. | WhatsApp + Walt + GHL | No orphaned conversation or screening session exists |
| 14 | Close today with exact counts for conversations, pre-qualified candidates, questionnaires, systems passes/retries, voice samples, AI role-plays, scorecards, human dispositions and interviews. Reconcile without double-counting before tomorrow’s volume. | Management review | Day-0 report, blockers and named next-action queue exist |
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.
| Role | Recording Type | What They Record | AI Grades For |
|---|---|---|---|
| Call Center Agent | Audio only | Voice recording demonstrating: icebreaker opening, confident tone, professional pacing, handling 2-3 objections, requesting information, and closing with a live transfer or appointment booking. | English native fluency, icebreaker ability, confidence/tone, professional pacing, objection handling, requesting information, closing (live transfer or appointment) |
| Closer (Sales) | Video | 60-90 second pitch: introduce themselves, sell solar, handle an objection. | Presence, confidence, solar knowledge, persuasion, professionalism, communication clarity |
| Canvasser | Video | Door approach pitch + appointment close. Appearance and presentation matter — they will be at people's doors representing the company. | Energy, friendliness, resilience, persuasion, physical appearance, presentation |
| QA | Audio only | Multiple assessments in one recording: (1) Act as an assistant following up to confirm the homeowner's information and appointment, (2) receive a live call transfer, conduct assessment and additional qualification confirmations, pass to the sales closer or book the appointment, (3) listen to a sample call recording and identify issues. | English native fluency, all call center agent skills (icebreaker, closing, objection handling), attention to detail, solar knowledge, analytical thinking, call quality assessment |
[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]| Criteria | Weight | Score 1-10 | What AI Evaluates |
|---|---|---|---|
| English native dialect | 30% | Must have excellent English with only very slight accent — native-level fluency, no communication barriers, natural cadence. Candidates who do not meet this standard cannot be considered. | |
| Icebreaking | 15% | Ability to get past the first 10 seconds of a call and persuade the contact to ENGAGE in speaking further — hooks attention, builds rapport instantly, prevents hang-ups | |
| Closing | 15% | As the call wraps up, adept at transitioning to a Live Transfer (preferred) OR booking an appointment — smooth handoff, clear next steps, does not let the call end without an outcome | |
| Objection handling | 10% | Responds naturally, doesn't freeze, stays on script, turns objections into engagement | |
| Technical skills | 10% | Ability to operate software in real-time while on a headset with the contact: ViciDial platform, GHL contact/submission form and calendar, Google Maps popup, Project Sunroof solar suitability popup | |
| Tone & energy | 10% | Friendly, confident, professional, enthusiastic — sounds like a helpful assistant, not a telemarketer | |
| Professionalism | 10% | No slang, no filler words, no inappropriate language, maintains composure under pressure | |
| Composite | 100% | /100 | Weighted average → stored as ai_screening_score. English dialect is a hard gate — candidates scoring below 7/10 on this criterion are auto-rejected regardless of composite score. |
| Criteria | Weight | Score 1-10 | What AI Evaluates |
|---|---|---|---|
| English native dialect | 30% | Must have excellent English with only very slight accent — native-level fluency, natural cadence. Hard gate: below 7/10 = auto-reject. | |
| Icebreaking | 10% | Hooks the homeowner's attention in the first 10 seconds — builds trust, creates engagement, prevents "not interested" | |
| Closing | 15% | Strong close: transitions smoothly to signed agreement or next appointment — creates urgency without being pushy, leaves with clear next steps | |
| Presence & confidence | 15% | On-camera comfort, eye contact, body language, professional demeanor | |
| Solar knowledge | 10% | Accurate information, handles technical questions, explains financing clearly | |
| Objection handling | 10% | Natural responses, doesn't get defensive, turns "no" into "let me explain" | |
| Professionalism & appearance | 10% | Appearance, language, demeanor — looks and sounds the part of a trusted advisor | |
| Composite | 100% | /100 | Weighted average → stored as ai_screening_score. English dialect is a hard gate — below 7/10 = auto-reject regardless of composite. |
| Criteria | Weight | Score 1-10 | What AI Evaluates |
|---|---|---|---|
| English native dialect | 30% | Must have excellent English with only very slight accent. Hard gate: below 7/10 = auto-reject. | |
| Physical appearance & presentation | 20% | Clean, professional appearance suitable for door-to-door representation. Approachable, well-groomed, company-branded look. | |
| Icebreaking | 15% | Ability to engage a stranger at their door within the first 10 seconds — friendly, non-threatening, builds immediate trust | |
| Closing | 15% | Gets the appointment or permission to follow up — does not leave without a next step | |
| Energy & resilience | 10% | High energy, positive attitude, handles rejection without losing momentum | |
| Objection handling | 10% | Addresses door-slam objections naturally, stays positive, pivots to value | |
| Composite | 100% | /100 | Weighted average → stored as ai_screening_score. English dialect and appearance are hard gates — below 7/10 on either = auto-reject. |
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.'"