An AI receptionist answers calls, books appointments, qualifies leads, and routes urgent calls to humans - 24/7, in under five seconds, with 90–95% resolution rates on routine inquiries. This page walks through how to build one on cto AI Business - using a Team Lead + specialist Team Members, phone gateway via MCP, and approvals on high-stakes actions. Free to pilot. Compares briefly to packaged services (Smith.ai, Goodcall, Rosie) at the end.
TL;DR
Shape it as an agent team:
- A Team Lead that owns the conversation and delegates per intent.
- Specialist capability for booking (calendar write), customer lookup (CRM read), knowledge-base Q&A, and escalation to humans.
- A phone gateway connection (Twilio, Vonage, or similar) so the team hears calls.
- Approvals on the high-stakes actions (appointment booking, outbound messages).
Hire the Members and pick assigned models per your priorities. Set approvals. Pilot on the free tier.
If you want plug-and-play rather than build, Smith.ai, Goodcall, Rosie, and Allo are the packaged services - covered at the end.
What a cto AI Business receptionist team looks like
You build the team in cto AI Business:
- Create the AI business. Plan: "Answer inbound calls 24/7, book appointments, route urgent calls to humans."
- Hire the Team Members you need. A typical receptionist team needs capability for booking, customer lookup, knowledge-base Q&A, and escalation. You decide how many Members to split that across - sometimes one well-scoped Member can hold two capabilities; sometimes you want strict role separation.
- Wire the phone gateway. Twilio, Vonage, or similar exposes inbound calls via MCP (community servers exist for the major providers).
- Set approvals. Anything customer-facing (booking, outbound messages) should require human sign-off during the first week of pilot. Loosen later.
- Pilot. Free tier covers low call volume; premium handles production.
What the Team Lead does live
When a call comes in:
- Your phone gateway MCP delivers the call transcript to the Team Lead.
- The Lead greets the caller, identifies intent (FAQ, booking, complaint, emergency).
- The Lead delegates to whichever Members handle that capability: knowledge-base Q&A, booking, customer lookup, escalation.
- Members work the subtask, return results to the Lead.
- The Lead returns to the caller with the answer and confirms next steps.
- Tasks are logged in the kanban for review.
Auto model picks per delegation so each step uses an appropriate frontier LLM for the work shape.
MCP integrations you'll wire
For a receptionist team, common integrations:
- Calendar - Google Calendar, Calendly, Cal.com. Community MCP servers exist.
- Customer DB - Supabase (pre-configured), Notion (pre-configured), or custom MCP for your CRM.
- Phone gateway - Twilio or Vonage via MCP (community + custom).
- Escalation - Slack, Linear (pre-configured).
- Knowledge base - Notion (pre-configured).
Everything pre-configured connects with one click after vendor authorization at cto.new → Integrations → MCP.
Why build on cto AI Business
- Free to pilot. Rolling 24h+7d window covers low call volume during validation. No commitment before you've proven the team works on your call mix.
- Real multi-agent. You scope what each Member can read and write. Safer than a single super-agent that has all the keys.
- Auto model. No need to pick a model per task. The gateway routes Sonnet for triage, Opus when stakes are high, Flash for latency-critical replies.
- Pause/resume. When you need to stop token usage immediately (e.g., during testing), pause from Headquarters.
Volume math
Packaged services charge per-minute (typically $0.75–$2.50/min) or per-month with caps. At 1,000 minutes/month:
- Smith.ai (hybrid): ~$1,500–$2,500/month.
- Allo (Business plan, unlimited): $45/seat/month.
- cto AI Business: Free for low volume; premium for production (flat-rate, no per-minute meter).
The arithmetic flips at high volume - packaged services with per-minute pricing get expensive; cto's flat-rate premium stays predictable.
When packaged is still the right call
Honest cases for buying:
- You want it running today. Smith.ai, Goodcall, Rosie set up in 30 minutes. Building takes 1–3 days.
- Standard call mix only - FAQ + appointments + basic intake - and standard integrations (Google Calendar, your CRM).
- You don't have technical capacity to wire phone gateway MCP servers.
Smith.ai is the strongest hybrid AI + human option. Rosie is the strongest pure-AI plug-and-play. Allo is the cheapest at flat pricing.
| Service | Plan starts | What you get |
|---|---|---|
| Smith.ai | ~$0.75–$2.50/min | AI + human hybrid |
| Goodcall | From ~$59/mo | Deep customization options |
| Rosie | From ~$49/mo | Fastest setup, polished UX |
| Allo | $25/seat/mo (Starter) | Flat pricing, no minute caps on Business |
| IsOn24 | From ~$39/mo | Simple setup, low touch |
| Trillet | Quote | SMB multi-channel |
| Air.ai | Quote | High-volume outbound + inbound |
What "good" looks like
Across 2026 benchmarks (347,609 real business calls):
- Resolution: 90–95% of routine calls handled without human help.
- Answer time: under 5 seconds.
- Caller sentiment: 99% positive.
Whatever you build or buy, test against these on your own call mix before going live. 50 real calls is the right pilot size before committing.
How to decide
FAQ
How much does it cost to build an AI receptionist on cto AI Business?
Free to pilot (ad-supported, rolling 24h+7d limits). Premium for production volume (flat-rate). You pay separately for the phone gateway (Twilio or Vonage usage).
How long does it take to build one?
1–3 days for a basic 4-agent team. Longer if you're wiring custom internal systems (your own CRM, custom booking) via MCP.
Can the receptionist handle HIPAA / legal compliance?
Carefully. HIPAA needs specific data-handling guarantees. Some packaged services (Smith.ai for legal, healthcare-focused vendors) handle compliance out of the box. Building gives you full control over the data path - but you take on the compliance responsibility.
What if my calendar / CRM doesn't have an MCP server?
Most popular tools have community MCP servers; custom MCP for your system is typically a few hundred lines of code. Or check cto.new → Integrations → MCP for newly added pre-configured options.
Can the AI receptionist do outbound calls too?
Yes - same architecture, the Team Lead initiates instead of receives. Common pattern: appointment reminders, follow-ups, lead-qualification call-backs. Approvals are stricter on outbound (deliverability + opt-in compliance).
Which models does the team use?
Auto model picks per task. Common pattern: Sonnet 4.6 for triage, Opus 4.8 for nuance, Gemini 3.5 Flash for latency-critical, GPT-5.3 Codex for code-heavy actions if the team integrates with code systems.