AI Workforce & Assistance
AI receptionist: every call answered, nothing decided that you haven't written down
The calls you're missing don't leave a record
Play one call
Call
Call record
- Intent
- —
- Service
- —
- Timing
- —
- Location
- —
- Area check
- —
- Slot
- —
- Status
- Call answered · after hours
Turn 1 of 6
What it's allowed to do
Does on its own
- Answers and greets
- Asks your qualification questions
- Checks service area and hours
- Offers open slots and books them
- Takes a message for a callback
- Saves the call record and summary
Asks a person
- Caller asks for a person
- The issue is urgent or out of scope
- The caller sounds frustrated
- Anything your rules don't cover
Never does
- ✕ Quotes a price you haven't approved
- ✕ Diagnoses a problem
- ✕ Promises what your rules don't allow
- ✕ Pretends to be a person when asked
Qualification rules, service areas, business hours, and escalation conditions live in configuration your team can change. The conversation can be natural; the decisions are not improvised.
How it fits together
Telephony routes the call to a voice AI runtime. The runtime handles the conversation; an orchestration layer carries out the resulting actions against your CRM, scheduler and notification channels. The important boundary is between the conversation and the record: the system saves the lead to its own database first, then queues and retries synchronization and surfaces anything that fails. A CRM outage should never mean a lost lead.
People take over when the rules say so
Live transfer
Callers can always reach a person. The conditions for transferring are explicit rules, not model guesswork.
Escalation
Calls the system can't resolve are flagged for a person, with the context already captured.
Review
Staff can read transcripts and outcomes, correct records, and adjust the rules.
Rule ownership
Your team decides what counts as qualified, which areas you serve, and what the system may book on its own.
Live transfer
- Reason
- caller asked for a person
- To
- on-call, East team
- Context passed
- summary so far, caller number, request
How you know it's working
01
Every call leaves a trail.
A transcript, a structured summary and an outcome, for every call, so nothing about a call is a mystery.
02
It's tested on your calls before it answers your phone.
We run it against recorded or scripted calls from your business and check the outcomes: right questions, right bookings, right transfers.
03
It's watched after launch.
Transfers, failed bookings and calls that ended badly are reviewed, and the rules or prompts are adjusted. Quality is measured on the job it does, not on the model's reputation.
Consent wording, call recording and transcript retention are reviewed for where you operate before launch.
What changes from one business to the next
- When the system answers: all calls, after hours only, or overflow.
- Qualification questions and service-area rules.
- What it can book directly, and what it captures for a callback.
- Transfer and escalation conditions, and who receives them.
- Which CRM, scheduler, and notification channels it updates.
- Consent wording, call recording, and transcript retention, reviewed for where you operate.
Technology we usually start from
A common starting point is Twilio for telephony; a voice runtime such as Vapi or Retell, or a direct voice architecture where that fits better; n8n or Trigger.dev for orchestration; PostgreSQL for the system's own state; and your existing CRM and scheduler, or Cal.com where there isn't one. These are starting candidates, not requirements. Your existing systems and constraints decide the final stack.
Is an AI receptionist the right system?
Good fit
- A meaningful share of new business arrives by phone.
- Calls are missed after hours, during jobs, or at peak times.
- Intake follows rules you can write down: service area, job type, urgency, availability.
- There's a CRM or scheduling system where the call outcome should land.
Not the right fit
- Most calls need expert judgment from the first sentence, such as diagnosis or negotiation.
- Call volume is low enough that a person can reliably answer every call.
- Consent, recording, and compliance requirements for AI-handled calls haven't been reviewed for your market.
- There's no agreed process for what happens after the call. Automating an undefined intake only moves the confusion somewhere else.
How we'd approach it
01
Understand
As much as the problem needs
We map how the work runs today: who does what, which systems hold the records, and where it breaks.
02
Design
Decided before it's built
Ownership, rules, integrations and exceptions, agreed with you and written down with acceptance criteria.
03
Build
Connected to what you use
We build the scoped system, connect it to the tools you already run, and test it on the cases that matter.
Questions about AI receptionists
Will callers know they're talking to an AI?
They should. The greeting can say so, and if a caller asks, the system answers honestly and offers a person. Disclosure rules vary by place and are reviewed before launch.
What if the caller wants a real person?
They get one. Asking for a person is a transfer condition, along with urgency, out-of-scope requests and frustration.
Can it work only after hours?
Yes. It can answer every call, only after hours, or only when the line is busy. Many businesses start with after hours and overflow.
What happens if the CRM or calendar is down during a call?
The call still ends with a saved record. The system keeps its own copy first and retries the CRM and calendar updates, and anything that keeps failing is flagged for a person.
Answer every call, even the ones you can't take
Tell us how calls are handled today, including after hours. We'll show you how the receptionist would handle the ones you miss.