# Sho'Nuff Front-Desk — AI Voice Lead Qualification & Booking

**Saved:** 2026-08-24
**Status:** Future Project — Competitive Teardown + Build Path
**Category:** SaaS Product / Revenue
**Owner:** IT Pro Partner (Sho'Nuff)
**Inspiration:** Qexo.ai competitive teardown (2026-08-24)

---

## The Competitive Trigger

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.

**Qexo's four-step loop:**

```
1. Capture    — web/form intake catches the lead while intent is hot
                message + contact + consent attached to ONE record
2. Understand — "lead intelligence" scores intent / urgency / service-fit
                0–100 qualification score, flags missing details
3. Call & Book — with consent, voice agent calls OUT, gets context,
                books against live calendar, confirms address/access notes
4. Manage     — unified lead workspace (inquiry + transcript +
                qualification + appointment), human override
```

**Why Qexo is clever (and what to steal):**
- **Full-context handoff** — the outbound call inherits the web intake, never starts over. The caller already knows what the lead asked for.
- **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.
- **"Not another inbox"** — explicitly positioned as NOT a CRM. A unified lead workspace, not a login you'll ignore.

---

## The Honest Read: We Already Own 90% of This

The gap is **productization, not capability.** We have every component running today:

| Component | Existing Asset | Status |
|---|---|---|
| Outbound voice calling | `shonuff-voice-caller`, `twilio-voice-calling` skills (Twilio + ElevenLabs) | ✅ Live |
| Inbound call handling | `ai-receptionist.md` (VoIPSimplicity Concierge) | 📐 Designed, not built |
| Missed-call capture | `missed-call-lead-recovery.md` (Twilio SMS text-back) | 📐 Designed, not built |
| Voice stack (STT→LLM→TTS) | `voice-agent-deployment` skill — Hermes Voice (xAI realtime) + Kokoro/faster-whisper open-source | ✅ Live |
| Orchestration brain | Hermes Agent (personality, memory, tool routing) | ✅ Live |
| Calendar | Rally family calendar + booking patterns | ✅ Live |

**The missing 10%** — the part that makes it a *product* rather than a demo:
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.
2. **A qualification scorer** — 0–100 intent/urgency/service-fit score with visible evidence (the lead's own words).

Neither is hard. Both are a weekend build on the infra we already run.

---

## Build Path

### Phase 1 — The Lead Record (the real moat)

Postgres table (single source of truth):

```
leads
  id, tenant_id, source (web/form/call/missed-call)
  contact_name, phone, email
  message (original inquiry, verbatim)
  consent_at (timestamp), consent_medium (form checkbox / verbal / none)
  qual_score (0-100), qual_evidence (json: flagged signals)
  service_fit, urgency, intent
  transcript (json, appended on call)
  appointment_id, appointment_status
  created_at, updated_at
```

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.

### Phase 2 — Qualification Scorer

DeepSeek (via admin-ai) classifies the lead record into a 0–100 score with visible evidence:

- **Intent** — did they ask for a specific service, or just browse?
- **Urgency** — "as soon as possible" / "this week" / "just looking"
- **Service-fit** — does the inquiry match any offered service?
- **Missing detail flags** — no address, no timeframe, no budget signal

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.

### Phase 3 — Outbound Call & Book

With consent on record, the voice agent calls out:
1. Pulls the lead record (never re-asks what the form already captured)
2. Confirms interest, fills the gaps the scorer flagged
3. Books against live calendar
4. Confirms address/access notes
5. Appends transcript + appointment to the lead record

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.

### Phase 4 — Unified Workspace ("Not another inbox")

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.

---

## Positioning vs. What We Already Have

| Product | Inbound | Outbound | Qualifies | Books | Key Differentiator |
|---|---|---|---|---|---|
| **VoIPSimplicity Concierge** (`ai-receptionist.md`) | ✅ answers calls | ❌ | ⚠️ basic | ✅ | Replaces IVR for existing VoIP customers |
| **Missed-Call Recovery** (`missed-call-lead-recovery.md`) | ⚠️ missed calls | ❌ | ❌ | ❌ | SMS text-back within seconds |
| **Sho'Nuff Front-Desk** (this) | ✅ | ✅ **calls out** | ✅ **scores** | ✅ | **Full-context handoff: web → score → outbound → calendar** |
| **Qexo.ai** (competitor) | ✅ | ✅ | ✅ | ✅ | The benchmark we're matching |

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.

---

## Pricing (Premium, Value-Based — Never Undercut)

Modeled on Qexo's SMB wedge but priced like we own the infrastructure (we do):

| Tier | Price/mo | Included |
|---|---|---|
| **Solo** | $99 | 1 voice number, web intake + scorer, 50 outbound calls, calendar booking |
| **Pro** | $299 | 3 numbers, 200 calls, multi-location, transcript archive, human-override console |
| **Managed** | $599+ | White-label, agency resell, custom qualification rules, SLA |

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.

---

## Risks

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.
2. **Voice quality at scale** — Kokoro is good but not ElevenLabs. Start managed-tier on ElevenLabs, offer Kokoro for beta cost control.
3. **Calendar write integrity** — a wrong booking is a lost customer. The lead record must be the single writer; no side-channel calendar edits.

---

## Open Questions

1. **First vertical** — solar/HVAC (Qexo's beachhead) vs. our existing warm markets (Debt Recovery Experts intake, VoIPSimplicity customers, Forefront Wireless)?
2. **Calendar backend** — Rally, or a dedicated booking calendar per tenant?
3. **Consent capture** — form checkbox (SMS/web) vs. recorded verbal consent (call). Both need a timestamped, auditable record.
4. **Tenant model** — multi-tenant from day one (agencies reselling to clients), or single-tenant until 3 paying customers?

---

## Next Steps

1. **Decide first vertical** (open question 1) — this shapes every downstream choice
2. **Build the lead-record schema** (Phase 1) — the moat, and a pure schema task
3. **Weekend spike**: qualification scorer on 10 sample leads, verify evidence layer
4. **Wire outbound call** via existing `shonuff-voice-caller` + Twilio
5. **Beta** with 1–2 friendly businesses before any pricing commitment

---

## Meta-Signal (worth remembering)

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.