This guide walks you through The Vapi Squad! setup by Henryk Brzozowski, showing how Make.com and Vapi create a multilingual AI voice receptionist for English, Spanish, and Polish. You’ll follow step‑by‑step demos of booking appointments, see live attempts in different languages, and find a transcript and Google Calendar inbound setup explained clearly.
The article outlines timestamps for the 3‑language demo, English and Polish booking attempts, the whole Make.com setup, squad configuration, and final notes and tips. Use these sections to reproduce the workflow, customize prompts, and streamline your inbound appointment automation.
Vapi Squad Concept and Purpose
What the Vapi Squad is and the problems it solves
The Vapi Squad is a modular, multilingual AI voice receptionist system you can deploy to handle inbound calls, book appointments, and capture transcripts without adding headcount. You use it to solve common problems like missed calls, inconsistent booking processes, and scaling support across languages. By automating routine conversational tasks you free your team to focus on higher-value work and reduce human error in scheduling and data capture.
Overview of multilingual AI voice receptionist use cases
You can apply the Vapi Squad across many scenarios: medical or wellness appointment booking, salon scheduling, small business reception, reservation systems, and lead qualification for sales. Because it supports multiple languages, you can serve customers in their native tongue, handle high call volumes after hours, and provide consistent availability for bookings and basic support. You also gain searchable transcripts for compliance, training, and analytics.
Inbound call handling versus outbound automation
Inbound call handling focuses on receiving customer-initiated calls — greeting the caller, identifying intent, capturing booking details, and confirming appointments. Outbound automation is proactive: reminders, follow-ups, or outreach to rebook. The Vapi Squad concentrates on inbound flows (the video demo shows multilingual inbound booking), but the same architecture can extend to outbound actions like automated reminders or re-engagement campaigns triggered from your calendar or CRM.
How Make.com and Vapi work together in this project
You connect Vapi as the conversational, real-time voice layer and Make.com as the orchestration and integration platform. Vapi handles ASR/TTS, dialog state, and assistant logic; Make.com receives webhooks from Vapi, executes business logic, queries availability, writes events to Google Calendar, stores transcripts, and handles retries and alerts. Together they form a robust pipeline where conversational intelligence and backend automation collaborate.
Business benefits: conversion, availability, and cost savings
With the Vapi Squad you increase conversion by reducing missed bookings and guiding callers confidently through service selection. You improve availability by staffing a 24/7 receptionist without hiring more people. Cost savings come from lower staffing needs, fewer no-shows through confirmations and reminders, and less time spent manually logging interactions. You also gain analytics that help optimize offerings and staffing over time.
Project Scope and Goals
Primary objective: reliable multilingual appointment booking
Your primary objective is to offer a reliable, consistent appointment booking experience in multiple languages. That means handling the full booking lifecycle — create, confirm, reschedule, and cancel — while ensuring data integrity and a natural conversational experience that reduces friction for callers.
Supported languages and reasoning for English, Spanish, Polish
You support English, Spanish, and Polish to cover broad business needs: English for global reach, Spanish for large regional populations and underserved markets, and Polish based on specific audience demand shown in the demo. These choices let you validate multilingual handling and showcase how language-specific tuning improves accuracy and customer satisfaction.
Functional goals: booking, rescheduling, confirming, capturing transcripts
Functionally, you want the assistant to collect appointment details (service, date/time, client name, contact), create calendar events, handle reschedules and cancellations, confirm details vocally, and capture clean transcripts. Each transaction should result in a calendar entry and a stored transcript for audit and follow-up.
Nonfunctional goals: latency, accuracy, privacy, and scalability
Nonfunctional goals include low latency responses (conversational pacing), high speech-recognition and intent accuracy, robust privacy controls (data minimization, encryption, retention policies), and the ability to scale to many concurrent calls and calendar writes. You aim for a balance between speed and comprehension.
Success metrics and KPIs to track
Track KPIs like bookings completed per hour, completion rate (calls that result in booked/rescheduled/cancelled events), average call duration, ASR confidence and intent accuracy, time-to-confirmation, error/fallback rate, transcript availability, and cost per booking. Monitor calendar conflicts and human escalation rates to detect breakdowns.
High-Level Architecture
Core components: Vapi voice layer, Make.com automation, Google Calendar
The architecture centers on three components: Vapi provides ASR/TTS and assistant orchestration, Make.com handles scenario automation and integrates services, and Google Calendar stores appointments. Supplementary components include storage for transcripts and logging, alerting, and optionally a CRM for client data.
Data flow from inbound call to calendar event and transcript storage
When a call arrives, Vapi routes it to the appropriate language assistant. Vapi collects slots and emits webhook events to Make.com at key points: slot-complete, booking-request, and call-end with final transcript. Make.com validates availability, performs freebusy queries, creates calendar events, stores transcripts in secure storage, and returns confirmation (and any messages) back to Vapi for voicing.
Third-party services and integrations required
You typically integrate with Google Calendar, cloud storage for transcripts, email/SMS providers for confirmations, and monitoring/alerting tools. Optionally integrate a CRM, payment gateway, or SMS reminders. Make.com modules cover HTTP calls, Google Calendar, storage, and notification services.
Where prompts, models, and URLs live in the architecture
Store prompts and assistant configuration inside Vapi assistants or a managed prompt repository referenced by Vapi. ASR/TTS model choices are configured per assistant. URLs for webhooks point to Make.com scenario endpoints. Keep a versioned prompt library in a central place (Make.com, Git, or a CMS) so both Vapi and your engineering team reference the same templates.
Failure domains and fallback design
Failure domains include ASR errors, webhook delivery failures, calendar write conflicts, and third-party outages. Design fallbacks: repeat or rephrase prompts for low confidence, offer to send a link or SMS for manual booking, queue a voicemail or human callback request if critical slots fail, and implement retry/exponential backoff for webhook processing. Ensure transparent escalation paths so callers aren’t left stranded.
Vapi Voice Assistant Setup
Creating and configuring Vapi assistants for each language
Create separate Vapi assistants per language to keep prompts, TTS, and ASR tuned. Configure each assistant’s greeting, slot definitions, error handling, and webhook endpoints. Map phone number routing so callers enter the right assistant, either by phone number, IVR selection, or language detection.
Selecting TTS and ASR models appropriate per language
Choose ASR models optimized for the target language and dialect to improve recognition. Select TTS voices with natural prosody and the right gender/tone for your persona. For Spanish and Polish you’ll want models that handle regional accents; for English, choose a model tuned to your primary caller population.
Setting voice personas and localized phrasing for natural interaction
Define a voice persona per language: friendly, professional, concise. Tailor phrasing and idioms to local conventions — how you ask for dates, how you confirm times, culturally appropriate greetings. Persona consistency reduces friction and increases trust.
Handling accents, phrases, and language-specific edge cases
Anticipate accents, code-switching, and slang. Add alternative phrasings for slots and synonyms for services. Build pronunciation guides and sample utterances into ASR models or prompts, and include fuzzy matching for names and places. Offer confirmation and spell-back for tricky inputs like names or email addresses.
Managing assistant variants and versioning
Version each assistant and keep a changelog of prompt and model updates. Use variants for experimental prompts or new features (A/B testing). Ensure you can rollback to a stable assistant version if a change increases errors.
Make.com Integration and Scenario Design
Designing scenarios to receive inbound events from Vapi
Design a modular Make.com scenario that begins with an HTTP webhook triggered by Vapi. Parse the payload, route based on event type (slot filled, booking request, call end), and branch into calendar checks, transcript storage, and notifications. Keep scenarios small and focused to aid debugging.
Key Make modules used and their roles (HTTP, Router, Filters, Google Calendar, Storage)
Use HTTP to accept Vapi webhooks and send responses. Use Router and Filters to branch flows by language, event type, and confidence. Employ Google Calendar modules for freebusy and event creation. Use storage modules to write transcripts and logs. Add notification modules for Slack/email for error alerts.
Mapping Vapi webhook payloads to Make.com flows
Map fields like call_id, assistant_id, language, slots, ASR interim/final text, and confidence to Make variables. Use slot names (name, date, time, service, phone) to drive calendar logic. Preserve timestamps and metadata for auditing and alignment with transcripts.
Retry logic, error branch flows, and alerting for failed scenarios
Implement retries for transient failures (HTTP timeouts, calendar API quotas) with incremental backoff. On permanent failures, route to an error branch that logs the event and triggers alerts to your ops channel. Capture failure context (payload, response codes) to speed troubleshooting.
Best practices for modular scenario organization and naming
Name modules and scenarios clearly by purpose and language (e.g., “Vapi_Webhook_EN”, “Vapi_Calendar_Create_ES”). Keep reusable sub-scenarios for calendar operations, transcript storage, and notifications. Document each scenario and version in your automation repository.
Google Calendar Sync and Appointment Logic
Authenticating and authorizing Google Calendar from Make.com
Authenticate with OAuth 2.0 and grant the scopes needed (read/freebusy, write events). Keep credentials secure and use a service account or a delegated account depending on your setup. Periodically refresh tokens and monitor for authorization errors.
Calendar selection, calendar colors, and calendar rules
Select the appropriate calendar for bookings (shared business calendar vs. individual staff calendars). Use colors or labels to indicate booking source or service type for easy human triage. Apply calendar rules such as working hours and blackout dates to avoid undesirable slots.
Finding available slots: freebusy queries and timezone handling
Use freebusy queries to fetch availability across relevant calendars and enforce service durations and buffers. Normalize caller timezones: detect from phone number or ask the caller, then convert to the calendar timezone. Confirm timezone explicitly during booking to prevent mistakes.
Creating events: required fields, descriptions, guests, reminders
Create events with clear titles, descriptions that include service details and contact info, and add guests if needed. Set reminders via calendar settings or additional notification services. Include metadata like source=Vapi and call_id to link events to transcripts and logs.
Handling conflicts, reschedules, cancellations, and double-booking prevention
When conflicts arise, present alternative slots in the dialog or auto-suggest next available times. For reschedules, remove or update the original event and notify guests. Use transactional checks (re-query freebusy immediately before write) to minimize double-booking. If write fails due to a race, surface human escalation or offer callback.
Multilingual Conversation Flows
Designing language-specific dialogues for booking, rescheduling, and canceling
Design natural flows that map user intents to slot collection steps. For booking: greet, confirm language, collect service and preferred times, confirm details, create event, and close. For rescheduling/canceling: verify identity, find the existing event, propose options, confirm changes. Tailor dialogs to the rhythm and politeness norms of each language.
Slot-filling strategy: name, date, time, service type, contact details
Use a guided slot-filling approach: ask one question at a time, validate input, and re-prompt when confidence is low. Prioritize critical slots (date/time, service) first, then collect optional details (email, notes). Offer choices for ambiguous inputs to accelerate completion.
Confirmation and repeat-back patterns to reduce errors
Always repeat back the core details: “You: June 12 at 3 PM for a haircut — is that correct?” Use short confirmations and allow quick correction paths. For low-confidence items, ask the caller to spell or confirm to reduce mistakes in calendar entries.
Fallbacks and escalation to human agent when intent confidence is low
If ASR or intent confidence remains low after retries, offer escalation: “I’m having trouble understanding — would you like me to connect you to a human?” Provide options for callback, voicemail, or scheduling via SMS link. Log escalation reasons for later improvement.
Example utterances and suggested localized prompts
Provide concise localized prompts:
- English booking greeting: “Hi, this is the booking line. What service would you like to schedule today?”
- Spanish booking greeting: “Hola, estás en la línea de reservas. ¿Qué servicio deseas agendar hoy?”
- Polish booking greeting: “Cześć, to linia rezerwacji. Jaką usługę chcesz umówić?” Use alternatives for confirmations, clarifications, and polite closings tuned to each language.
Transcripts: Capture, Format, and Storage
Capturing real-time ASR output and final transcripts from Vapi
Capture interim ASR for live interactions and final ASR for persistent transcripts. Have Vapi emit events containing timestamps, speaker labels, and confidence scores so you can reconstruct the conversation and correlate it with calendar events.
Formatting transcripts: plain text, JSON, SRT/VTT for timestamps
Store transcripts in multiple formats: plain text for quick search, structured JSON with metadata and confidence for analysis, and SRT/VTT if you need timestamped captions for recordings. Include utterance-level timestamps to enable precise QA and clipping.
Language tagging and translation of transcripts to English
Tag each transcript with the detected language and store the original. Optionally translate transcripts to English for centralized review and analytics. Keep both original and translated text to preserve accuracy and legal compliance.
Storing transcripts securely: cloud storage options and retention policies
Store transcripts in encrypted cloud storage with access controls. Implement retention policies aligned with privacy and regulatory requirements (e.g., delete after X months unless flagged). Log access for auditing and ensure backups are encrypted.
Using transcripts for QA, training, and analytics
Use transcripts to measure intent accuracy, identify common failure points, refine prompts, and retrain models. Aggregate patterns around misunderstood phrases, frequent reschedules, or service demand. Transcripts are invaluable for continuous improvement and compliance.
Prompt Engineering and Prompt Library
Core prompt patterns for greeting, slot collection, confirmation, and error handling
Build a prompt library with core patterns: greeting, slot prompts, clarifiers, confirmations, low-confidence re-prompts, and fallbacks. Keep prompts concise, polite, and directive when needed. Include variations to avoid robotic repetition.
Language-specific prompt templates for English, Spanish, and Polish
Create localized templates reflecting natural phrasing and etiquette. For example, Spanish may use more formal phrasing with usted depending on your audience; Polish might prefer concise phrasing. Keep translations reviewed by native speakers to avoid awkwardness.
How to include context: calendar availability, previous interactions, service list
Inject context into prompts when relevant: “I see you previously booked a service with us last month; would you like the same service?” or “We have openings on Tuesday and Thursday morning.” Supply availability and service lists dynamically so the assistant can offer real-time options.
Versioning prompts and experimenting with variations
Version your prompt library and run experiments to test different phrasings and confirmations. Track impact on KPIs like completion rate and average call duration. Roll back if an experiment degrades performance.
Examples of prompts used in the demo and when to tweak them
Use the demo prompts as starting points: friendly greetings, succinct slot questions, and explicit confirmations. Tweak prompts when you see repeated misunderstandings, cultural mismatches, or when caller behavior changes (e.g., shorter attention spans demand even briefer prompts).
Conclusion
Summary of the Vapi Squad approach and its business value
The Vapi Squad offers a practical blueprint to automate multilingual inbound call handling, combining Vapi’s conversational voice capabilities with Make.com’s orchestration and Google Calendar persistence. You gain higher conversion, 24/7 availability, and operational savings while capturing rich transcripts for optimization.
Key next steps to replicate and customize the setup
Start by creating language-specific assistants in Vapi, build a modular Make.com scenario for webhook handling and calendar integration, and configure Google Calendar with correct scopes. Pilot with a small volume of callers, measure KPIs, and iterate on prompts and flows.
How to use transcripts and Google Calendar integration effectively
Link transcripts to calendar events via call_id metadata, use transcripts for QA to improve prompts, and leverage calendar data to inform dynamic prompts (available slots, working hours). Automate reminders and confirmations to reduce no-shows.
Encouragement to iterate: measure, learn, and improve
Treat your Vapi Squad as an experimental system: measure real usage, learn from failures, and iteratively improve prompts, ASR/TTS selection, and automation logic. Small, frequent improvements yield large gains in user experience and reliability.
Pointers to the transcript, prompt library, and demo timestamps for hands-on follow-up
Keep an indexed repository of transcripts, a versioned prompt library, and timestamped demo notes so you can reproduce demo behaviors and test changes. Use the demo timestamps and recorded sessions as reference points to validate flows and train staff who will monitor or escalate calls.
Enjoy building your Vapi Squad — iterate boldly, keep the experience human-friendly, and use transcripts and calendar integrations to continuously refine the receptionist that never sleeps.
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