AI receptionist workflows for ops teams

An AI receptionist for your inbound queue

CommanderGPT triages, routes and drafts replies to every inbound request with slash commands. It handles the written inbox, not the phone line.

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Ops lead triaging a sorted inbound inbox at a modern front desk
What it covers

The reception work that eats your ops week

A receptionist does four jobs: greet, sort, route, follow up. Each one maps to a command your team can run today.

Triage on arrival

Run /triage on any inbound email, form fill or Slack request. It tags intent, urgency and account, so nobody reads the whole queue to find the hot one.

Routing rules in plain words

Write the rules the way you would brief a new hire. Pricing questions go to sales, billing goes to finance, outages go to CS. The command applies them every time.

First reply drafted

/draft-email prepares an on-brand first response using the account context already pulled. You edit two lines instead of writing from a blank page.

30-day context memory

The agent remembers prior threads with the same contact for 30 days. A returning lead is greeted as a returning lead, not a stranger.

Team Playbooks

Save the chain once and share it with /share. Everyone covering the shared inbox runs the same sequence, versioned, so an edit reaches the whole team.

Multi-model routing

Send classification to a fast model and the customer-facing draft to a stronger one. Claude, GPT-4o or Gemini, set per command.

How it works

From inbound message to routed reply in four steps

The chain is the same for a demo request, a support question or a partnership pitch.

  1. 1

    Paste or forward the request

    Drop the email, form payload or Slack thread into CommanderGPT, or trigger the command from the channel where it landed.

  2. 2

    Run /triage

    The agent classifies intent and urgency, pulls account context from the last 30 days, and names the right owner based on your routing rules.

  3. 3

    Get the brief and the draft

    /summarize writes the two-line brief for the owner. /draft-email prepares the first reply, with the open questions flagged.

  4. 4

    Review and send

    You read the output, fix what is off, and send. The agent never sends on its own. The judgment call stays with a person.

Use case: sales ops

Inbound lead triage without the shared inbox scramble

A sales ops lead covers a shared inbox that receives demo requests, partnership pitches and misdirected support questions. Before the playbook, three people skimmed every message and replied from memory. With a saved /triage chain, each message gets a category, an owner and a draft reply within the same run. The qualified demo requests surface first, with the company context attached. Measure your own before and after: log the minutes spent sorting for one week, then compare after two weeks of the playbook. The agent does not decide who is a good lead. It applies the rules you wrote and shows its reasoning, so you can correct the rules when it gets one wrong.

  • Every message tagged with intent, urgency and owner
  • Demo requests surface first with account context
  • Rules are editable text, not buried settings
See the /triage chain
Sales ops manager reviewing triaged inbound requests with priority tags
Use case: customer success ops

One shared playbook for the whole front line

A CS ops team of several people takes turns on the front line each week. The problem was never the tool, it was consistency: each person had a personal prompt and tone. A Team Playbook fixes that. One person builds the triage and reply chain, tests it on past tickets, then shares it with /share. When the escalation rules change, the owner edits the playbook once and the next run uses the new version for everyone. Limits worth knowing: the agent only sees what you give it, so messages that depend on a phone call or a screen share need a human note before the chain runs.

  • Fork, test on past tickets, then share
  • Versioned: one edit updates the whole team
  • Escalation rules live in the playbook, not in someone's head
See Team Playbooks
Customer success team reviewing a shared inbound playbook on a monitor
Where it fits

Written-inbox agent versus a voice receptionist

Two different jobs share the same name. Pick the one that matches where your inbound actually arrives.

CriteriaCommanderGPTVoice AI receptionist servicesHuman front desk or VA
Answers live phone callsNoYes, that is the core productYes, within working hours
Triage of email, forms and SlackYes, via /triageUsually limited to call summariesYes, but manual and slower at volume
Shared routing rules for a teamTeam Playbook, versionedConfigured per vendor dashboardLives in documentation and habit
Multi-model choice per stepClaude, GPT-4o or GeminiVendor-selectedNot applicable
Human review before sendAlways, by designVaries by productNative
Questions

What ops leads ask before they set it up

Does CommanderGPT answer phone calls like an AI receptionist?
No. CommanderGPT is a slash command workspace, not a voice or telephony product. It does not pick up calls or run a phone line. It handles the written side of reception: triaging inbound emails, form fills and Slack requests, drafting replies, and routing each one to the right owner. If you need live call answering, pair a voice service with CommanderGPT for everything that happens after the call.
What does an AI receptionist workflow look like for an ops team?
Three commands. Run /triage on a new inbound request to classify intent and urgency, /summarize to produce a two-line brief for the owner, then /draft-email to prepare the first reply. You review the output and send. Teams usually save the chain as a Team Playbook so every shared inbox follows the same sequence.
Can it book meetings or update my CRM automatically?
It prepares the work, you approve it. The agent can pull account context, propose time slots from the details in the request, and draft the confirmation or CRM note. Nothing goes external without your review, which keeps wrong-routing mistakes out of customer-facing channels.
How is this different from using ChatGPT for inbound replies?
ChatGPT works well for a single reply. CommanderGPT adds chained commands, a Team Playbook shared across people, and context memory that persists for 30 days across sessions. The delta shows up when five people handle the same inbox and need the same triage rules, not five slightly different prompts.
Which models run behind the commands?
You can route each command to Claude, GPT-4o or Gemini. Triage and classification can run on a fast model while the customer-facing draft runs on a stronger one. You set the mix once and the routing applies on every run.
Is it safe to put customer messages through it?
Treat it like any other tool that sees customer data. Check your own data handling policy, limit what you paste to what the task needs, and keep a human review step before any reply leaves the building. Do that review on the first 50 runs of any new playbook before you trust it.
How long does setup take?
A basic triage chain takes under an hour to configure, mostly spent writing your routing rules in plain language. Measure your own baseline first: count how many minutes your team spends sorting inbound for one week, then compare after the playbook runs for two weeks.
Who is this a good fit for, and who should skip it?
It fits ops leads in GTM, sales ops and customer success who already work in Slack, Notion or Linear and want consistent handling of inbound requests. Skip it if your main need is a 24/7 voice agent for a clinic, restaurant or law office front desk. A dedicated voice product serves that need better.

Your next command to set up

Start with /triage on ten real inbound messages from last week. Compare the output with how your team actually routed them, then fix the rules.

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