Sync docs, audit artifacts, project notes, and VerdictTank proposal docs
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# Sho'Nuff Front-Desk — AI Voice Lead Qualification & Booking
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**Saved:** 2026-08-24
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**Status:** Future Project — Competitive Teardown + Build Path
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**Category:** SaaS Product / Revenue
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**Owner:** IT Pro Partner (Sho'Nuff)
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**Inspiration:** Qexo.ai competitive teardown (2026-08-24)
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---
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## The Competitive Trigger
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Qexo.ai is AI voice agents for **lead qualification + appointment booking**, aimed at home-services / SMB verticals (solar, HVAC, home services, real estate, insurance, clinics). It is the third tool in a row we've teardown'd (after tranx.io and dozier.io) shaped as a single-purpose vertical tool on generic AI plumbing: narrow SMB wedge + clean before/after loop + evidence/confidence. The pattern is now a signal, not a coincidence.
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**Qexo's four-step loop:**
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```
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1. Capture — web/form intake catches the lead while intent is hot
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message + contact + consent attached to ONE record
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2. Understand — "lead intelligence" scores intent / urgency / service-fit
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0–100 qualification score, flags missing details
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3. Call & Book — with consent, voice agent calls OUT, gets context,
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books against live calendar, confirms address/access notes
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4. Manage — unified lead workspace (inquiry + transcript +
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qualification + appointment), human override
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```
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**Why Qexo is clever (and what to steal):**
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- **Full-context handoff** — the outbound call inherits the web intake, never starts over. The caller already knows what the lead asked for.
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- **Transparent AI + consent** — kills the FTC/legal objection up front. Voice agents calling out is the single most legally exposed move in this space; Qexo neutralizes it by design.
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- **"Not another inbox"** — explicitly positioned as NOT a CRM. A unified lead workspace, not a login you'll ignore.
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---
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## The Honest Read: We Already Own 90% of This
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The gap is **productization, not capability.** We have every component running today:
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| Component | Existing Asset | Status |
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|---|---|---|
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| Outbound voice calling | `shonuff-voice-caller`, `twilio-voice-calling` skills (Twilio + ElevenLabs) | ✅ Live |
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| Inbound call handling | `ai-receptionist.md` (VoIPSimplicity Concierge) | 📐 Designed, not built |
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| Missed-call capture | `missed-call-lead-recovery.md` (Twilio SMS text-back) | 📐 Designed, not built |
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| Voice stack (STT→LLM→TTS) | `voice-agent-deployment` skill — Hermes Voice (xAI realtime) + Kokoro/faster-whisper open-source | ✅ Live |
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| Orchestration brain | Hermes Agent (personality, memory, tool routing) | ✅ Live |
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| Calendar | Rally family calendar + booking patterns | ✅ Live |
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**The missing 10%** — the part that makes it a *product* rather than a demo:
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1. **A lead-record model that survives web → voice → calendar** — one object holding intake message, contact, consent, qualification score, transcript, and appointment, all keyed to the same lead. This is Qexo's actual moat, and it's a schema problem, not an AI problem.
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2. **A qualification scorer** — 0–100 intent/urgency/service-fit score with visible evidence (the lead's own words).
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Neither is hard. Both are a weekend build on the infra we already run.
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---
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## Build Path
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### Phase 1 — The Lead Record (the real moat)
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Postgres table (single source of truth):
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```
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leads
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id, tenant_id, source (web/form/call/missed-call)
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contact_name, phone, email
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message (original inquiry, verbatim)
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consent_at (timestamp), consent_medium (form checkbox / verbal / none)
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qual_score (0-100), qual_evidence (json: flagged signals)
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service_fit, urgency, intent
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transcript (json, appended on call)
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appointment_id, appointment_status
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created_at, updated_at
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```
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Every downstream step (scorer, outbound call, calendar write) reads and writes **this same record**. The lead never starts over. This is the "full-context handoff" Qexo sells, reduced to a schema.
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### Phase 2 — Qualification Scorer
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DeepSeek (via admin-ai) classifies the lead record into a 0–100 score with visible evidence:
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- **Intent** — did they ask for a specific service, or just browse?
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- **Urgency** — "as soon as possible" / "this week" / "just looking"
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- **Service-fit** — does the inquiry match any offered service?
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- **Missing detail flags** — no address, no timeframe, no budget signal
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Output: a score + the exact phrases that drove it. The evidence layer is what makes it defensible against "AI made that up" — the customer sees the lead's own words backing the score.
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### Phase 3 — Outbound Call & Book
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With consent on record, the voice agent calls out:
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1. Pulls the lead record (never re-asks what the form already captured)
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2. Confirms interest, fills the gaps the scorer flagged
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3. Books against live calendar
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4. Confirms address/access notes
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5. Appends transcript + appointment to the lead record
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Reuse `shonuff-voice-caller` (ElevenLabs professional male voice) + Twilio outbound. The open-source Kokoro/faster-whisper stack from `voice-agent-deployment` is the $0-cost alternative for beta.
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### Phase 4 — Unified Workspace ("Not another inbox")
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Single view per lead: original inquiry + qualification score + transcript + appointment, human override everywhere. Not a CRM — a workspace where a lead either gets booked or gets a reason why not.
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---
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## Positioning vs. What We Already Have
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| Product | Inbound | Outbound | Qualifies | Books | Key Differentiator |
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|---|---|---|---|---|---|
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| **VoIPSimplicity Concierge** (`ai-receptionist.md`) | ✅ answers calls | ❌ | ⚠️ basic | ✅ | Replaces IVR for existing VoIP customers |
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| **Missed-Call Recovery** (`missed-call-lead-recovery.md`) | ⚠️ missed calls | ❌ | ❌ | ❌ | SMS text-back within seconds |
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| **Sho'Nuff Front-Desk** (this) | ✅ | ✅ **calls out** | ✅ **scores** | ✅ | **Full-context handoff: web → score → outbound → calendar** |
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| **Qexo.ai** (competitor) | ✅ | ✅ | ✅ | ✅ | The benchmark we're matching |
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The Front-Desk is the top of the funnel the other two feed into. Missed-call recovery captures the lead; the Front-Desk qualifies and books it. These are three products on one voice stack, not three competing ideas.
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---
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## Pricing (Premium, Value-Based — Never Undercut)
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Modeled on Qexo's SMB wedge but priced like we own the infrastructure (we do):
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| Tier | Price/mo | Included |
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| **Solo** | $99 | 1 voice number, web intake + scorer, 50 outbound calls, calendar booking |
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| **Pro** | $299 | 3 numbers, 200 calls, multi-location, transcript archive, human-override console |
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| **Managed** | $599+ | White-label, agency resell, custom qualification rules, SLA |
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Undercut Qexo on **unit economics**, not headline price. Our marginal cost is near-zero (self-hosted voice stack, admin-ai tokens at cost). Qexo pays per-call infrastructure margins we don't.
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---
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## Risks
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1. **FTC / TCPA on outbound AI calls** — the single biggest exposure. Qexo's consent-first design is correct and mandatory. We mirror it: no outbound call without a recorded consent timestamp. `debt-recovery-compliance` skill already documents the TCPA/consent discipline; reuse it.
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2. **Voice quality at scale** — Kokoro is good but not ElevenLabs. Start managed-tier on ElevenLabs, offer Kokoro for beta cost control.
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3. **Calendar write integrity** — a wrong booking is a lost customer. The lead record must be the single writer; no side-channel calendar edits.
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---
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## Open Questions
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1. **First vertical** — solar/HVAC (Qexo's beachhead) vs. our existing warm markets (Debt Recovery Experts intake, VoIPSimplicity customers, Forefront Wireless)?
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2. **Calendar backend** — Rally, or a dedicated booking calendar per tenant?
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3. **Consent capture** — form checkbox (SMS/web) vs. recorded verbal consent (call). Both need a timestamped, auditable record.
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4. **Tenant model** — multi-tenant from day one (agencies reselling to clients), or single-tenant until 3 paying customers?
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---
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## Next Steps
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1. **Decide first vertical** (open question 1) — this shapes every downstream choice
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2. **Build the lead-record schema** (Phase 1) — the moat, and a pure schema task
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3. **Weekend spike**: qualification scorer on 10 sample leads, verify evidence layer
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4. **Wire outbound call** via existing `shonuff-voice-caller` + Twilio
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5. **Beta** with 1–2 friendly businesses before any pricing commitment
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---
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## Meta-Signal (worth remembering)
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Three teardowns in a row — tranx.io, dozier.io, qexo.ai — are all **single-purpose vertical tools on generic AI plumbing**, each with the same shape: narrow SMB wedge, clean before/after loop, evidence/confidence layer. The pattern means the plumbing is commoditizing. The defensible layer is not the AI — it's the **data model and the compliance posture** (lead record + consent + calendar integrity). We already own the plumbing. The win is in the schema, the scorer evidence, and the consent design — not in out-building the AI.
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