An AI Receptionist For Your Inbound Customers
AI Front Office is the customer-facing intake layer for service businesses. The AI receptionist qualifies inbound web chat and SMS — checks service-area coverage, matches to your catalog, finds the next available window, and creates a booking that goes to dispatcher review. Speed-to-Lead SLA tracking, online booking widget on a tenant subdomain, B2C win-back campaigns, and Call Coach AI scoring round out the front-office stack.
What AI Front Office Helps You Do
Six everyday capabilities for the front of the house.
Answer Inbound 24/7
The AI receptionist greets visitors over web chat and SMS. After-hours-only mode lets humans handle business hours while the agent covers nights and weekends.
Qualify And Book
The agent checks service-area coverage, matches the request to your service catalog, finds the next available window, and creates a BookingRequest tied to the conversation.
Quote Pricing Ranges
The receptionist never quotes an exact price sight-unseen. It quotes the range from your catalog plus trip charge — by design, to protect the truck-roll math.
Hand Off To Dispatchers
Every booking lands in the dispatcher review queue. Approve, decline, bulk-decline, or export. Shadow mode lets you run end-to-end without exposing the queue to dispatchers during rollout.
Track Speed-To-Lead
A configurable SLA from inbound to first contact. Warnings fire at 25% remaining; breaches surface in the dispatcher queue with cost and ROI in the rollups.
Win Back Lost Customers
TCPA-compliant SMS/email campaigns to dormant customers, declined estimates, and missed calls. Consent, STOP-keyword handling, and suppression lists keep outreach governed.
Three Roles, One Front Office
Each role gets its own view of the same conversation graph.
For Customers (Public)
A public booking widget lives at `book.{your-slug}.workforce.mangoapps.com` — branded to you, no MangoApps login required.
- Web chat — AI receptionist on your branded widget.
- SMS intake — inbound requests via Twilio with the same dispatcher review model.
- Online booking — non-AI form path for visitors who'd rather click than chat.
- Knowledge base — pre-booking Q&A grounded in your FAQ articles, never invented.
For Dispatchers
A queue purpose-built for the "what's waiting for me" question.
- Booking request review — approve, decline, or bulk-decline with reason codes.
- Urgent push — same-day and emergency requests fire a mobile push automatically.
- Sentiment escalation — sustained customer frustration auto-routes to a human.
- Field Service handoff — convert approved requests into Field Service work orders.
For Owners & Admins
Configuration that makes the system safe to roll out, observable while live, and tunable on cost.
- Shadow mode — agent runs end-to-end but bookings are hidden from dispatchers.
- After-hours only — humans handle business hours; agent covers nights and weekends.
- Cost caps — per-conversation token budget and per-channel monthly caps.
- Confidence floors — minimum geocode confidence required before auto-book is eligible.
- Roles & permissions — separate allowed_roles for review vs. configuration.
Connected To The Rest Of MangoApps
AI Front Office shares conversations, bookings, and customers with the apps that turn intake into work.
→ Field Service Suite
Approved BookingRequests become WorkOrders in Field Service Suite — the truck-roll, technician scheduling, and invoicing motion picks up where intake left off.
See Field Service→ Mango Spend
Conversation cost guardrails and campaign spend controls help owners understand intake ROI.
See Mango Spend→ Workforce Bots
The receptionist's pre-booking Q&A is grounded in your knowledge base — the same KB you can stand up with Workforce Bots.
See Workforce Bots→ Notifications
Urgent bookings, SLA breaches, and dispatcher pings flow through the standard notification system.
See NotificationsREPLACES POINT TOOLS
One front office in place of the receptionist-AI patchwork
Service businesses today bolt together a chat widget, a separate SMS responder, a voice-AI receptionist, and a booking page — each with its own price, its own data model, and zero awareness of the others. AI Front Office is one inbound surface across chat, SMS, and voice with the customer, booking, and dispatch graph already attached.
Hyro
Conversational AI for inbound calls and chat, sold by channel
- Same agent handles chat, SMS, and voice — no per-channel SKU or per-channel build
- Bookings, customers, and follow-up tasks live in the same DB the dispatcher already uses
- Per-employee pricing covers the whole workforce stack, not just the receptionist layer
Dialpad AI
AI on top of a separate phone system
- You don't have to be a Dialpad customer — keep your existing number, port it, or use the bundled one
- The AI books the job and dispatches the tech in the same flow, not just summarizing the call after
- One audit trail across chat, SMS, voice — Dialpad sees only the phone leg
Ada / Intercom Fin
Web-chat AI assistants
- Picks up the call the chat widget couldn't deflect — same conversation graph, no handoff loss
- No per-resolution metering — predictable cost as inbound volume scales
- The same agent that answers also dispatches a technician and texts the ETA
Google Dialogflow CX
Build-your-own conversational platform
- You don't need a CX team — the agent is configured in the same admin every other MangoApps app uses
- Booking, customers, and tickets are first-class entities, not external integrations you maintain
- Confidence thresholds, fallback humans, and audit are wired by default
Generic "AI receptionist" SaaS
Single-purpose receptionist tools
- The receptionist is one app in a workforce platform you already run — not a 9th vendor invoice
- Replaces the receptionist, the booking widget, the dispatcher view, and the customer record at once
- Same SSO, audit, and retention as the rest of MangoApps — nothing new for security to re-certify
PLATFORM LEVERAGE
AI Front Office inherits everything else MangoApps already does
A point receptionist tool has to build, buy, or integrate each of these. AI Front Office gets them for free because the platform already runs them.
Identity & SSO
Dispatchers and technicians sign in with the same SAML/OIDC SSO and MFA as the rest of MangoApps. No separate receptionist login to provision or revoke.
Customer & booking graph
Customers, bookings, tickets, and contacts are first-class records in the platform — not data trapped inside a chat-widget vendor's silo.
Workforce dispatch
The same Field Service dispatch board, technician roster, and ETA calculator the company already uses — the receptionist routes into it directly.
Audit log & retention
Every chat, SMS, and call transcript lands in the same audit log and retention policy Legal already approved for the rest of the platform.
Mobile app & push
Dispatchers and field techs see the booking come in on the branded mobile app they already use for shifts and pay — no second app to install.
Workflow & automation
A confirmed booking triggers a dispatch task, a customer SMS, and a follow-up form using the same automation engine as every other app.
INDUSTRY FIT
Built for the service industries where missed calls are missed revenue
AI Front Office is the inbound layer for service businesses where every after-hours call is a job won or lost — and where the dispatcher is already overloaded.
Home Services
HVAC, plumbing, electrical, and pest control take the after-hours call, book the slot, and dispatch a technician — without a human picking up.
Healthcare Clinics
Appointment requests, prescription refill triage, and intake forms handled in chat / SMS / voice — with HIPAA-aware audit and clinician escalation.
Auto & Repair
Service-advisor bookings, status updates, and pickup-ready texts run on the same surface — without an extra DMS plugin.
Real Estate & Property Mgmt
Showing requests, maintenance tickets, and lease-renewal questions route to the right agent or technician with full address + tenant context.
Professional Services
Inbound leads from chat, SMS, and the website all funnel into one qualification flow — booking the right consultant on the right calendar.
Field Service & Trades
Job intake from existing customers and net-new prospects flows into the same dispatch board the office already runs.
WHY MANGOAPPS WINS
One platform beats a stack of point solutions on every axis
The argument the owner, the dispatcher, the technician, and finance all share — and the one a single-purpose receptionist tool structurally cannot answer.
Cheaper than the stack
One per-employee suite license replaces the chat widget + SMS responder + voice AI + booking tool — and the bill stops growing per channel.
More secure
One identity perimeter, one audit log, one retention policy across every channel. Customer data never leaves the platform.
Easier to deploy
Turn the app on, point an inbound number at it, and the AI receptionist starts taking calls the same day — booked into existing dispatch.
Easier to use
Dispatchers and field techs see new bookings inside the same MangoApps mobile app they already use for shifts, leave, and pay. One login.
Easier to manage
Confidence thresholds, escalation rules, and after-hours behavior live in one admin — not split across four vendor portals.
Easier to extend
A confirmed booking can fire a dispatch task, a form, a Bot, or an e-signature flow — using the same automation engine as every other app.
AI is actually better
The agent sees the customer's full history, the technician's calendar, and the dispatch board in one graph — the only reason it can actually book the job instead of just summarizing the call.
AI Front Office In Practice
A practical scope check: what the app covers, which controls matter, and the workflows teams usually run first.
Core workflow
Web chat widget on tenant subdomain (book.{slug}.workforce.mangoapps.com).
Controls that matter
Compliance & Safety includes Two-party call recording consent (state-aware disclosure) and TCPA opt-in / STOP / suppression workflow.
Scope and specs
Scope note: web chat and inbound SMS are the default supported intake channels; voice intake is gated separately and should be planned as a separate rollout path.
AI-assisted web intake
Add a web chat widget on the tenant booking subdomain, qualify the request, validate service area, and create a BookingRequest for dispatcher review or eligible auto-booking.
Speed-to-lead dispatch
Route booking requests by service type, zip code, and urgency while tracking SLA warnings and breach notifications so inbound demand does not sit unseen.
Win-back and coaching
Build dormant-customer or missed-call audiences, draft compliant SMS/email outreach, and score calls for greeting, diagnosis, booking attempt, and handling quality.
Pair AI Front Office With Its Agent
The receptionist itself IS an agent — but Ask AI also exposes a dispatcher-side query agent for the "show me today's queue" kind of question.
AI Front Office
Inbound intake agent for chat and SMS — qualifies, books, and routes to dispatchers.
Customer Success
How Service Businesses Use It
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
Customer Case Studies
At our head office within HR, marketing, and finance, we use ChatGPT quite a bit, and MangoApps AI Helper is a useful tool for us. As I go around to the stores, I’ve been showing them how the AI Intranet Helper can find things and answer plain language questions about our resources, and they’re quite excited about that.
Alison van Zijl
Head of Human Resources
Full House
Frequently Asked Questions
Mango GTM is the B2B SaaS sales funnel for the tenant business itself — Product Advisor, demo booking, lead scoring, outbound prospecting. AI Front Office is the B2C front desk for the tenant business's own end-customers — a homeowner calling a plumbing co, a customer booking a salon appointment. Different audiences, different motions, different agents.
No, by design. The receptionist quotes a range from your service catalog plus a trip charge. Exact pricing requires diagnostics on-site — the system is explicit about that to protect the truck-roll economics.
Every booking lands in dispatcher review by default. You can run in shadow mode during rollout (the agent processes the conversation end-to-end but bookings are hidden from dispatchers). After rollout, dispatcher approval can be required for every booking, or only for bookings below a confidence threshold.
Web chat on a public branded subdomain and inbound SMS via Twilio are supported. Voice intake is gated separately and should be treated as an optional rollout path, not the default v1 channel.
The agent gets a `lookup_knowledge_base` tool when the Knowledge Base config is on. It searches your authored FAQ articles before escalating — "do you service brand X?", "what's the warranty?", "are you licensed in Texas?" — and is told to never invent an answer.
Approved BookingRequests can be converted into Field Service Suite WorkOrders, so dispatch, technician scheduling, and invoicing pick up where intake ends. See Field Service.
Win-back is opt-in per customer and per channel, with documented consent and revocation flow. SMS and email are governed by TCPA consent handling, STOP-keyword processing, suppression lists, and campaign controls.
Yes. Every BookingRequest carries the conversation transcript, the matched service, the geocode confidence, the SLA window, the sentiment trace, and the urgent / non-urgent flag. Dispatchers approve or decline with full context.
The Ask AI agent is the dispatcher-side query layer — "show me today's queue", "what's our Speed-to-Lead this week?", "summarize the last 24 hours of inbound". The receptionist itself is the customer-facing agent. See the agent page.
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