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H2· Hospitality and tourism

AI Guest Experience & Ops Automation

You will design a concierge that answers guests in their language and hands the risky questions to a human, and an ops flow that gets a broken AC fixed before the guest complains twice.

Choose a planRead the syllabus
Who it is for
BSc Hotel Management and Airlines/Tourism students heading for front office, guest relations or operations, who want to run the tools instead of being run by them.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 2.3 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Front Office Associate
  • Guest Relations Associate
  • Guest Experience Executive
  • Operations Coordinator

The skills these roles ask for. Preparation, not a promise of a job.

02Tools you will use
  • Claude (free plan)Free tier
  • WhatsApp-style concierge simulator (Saarthi sandbox)Saarthi sandbox
  • WhatsApp Business appFree tier
  • n8n Community (Saarthi sandbox)Saarthi sandbox
  • Google SheetsFree tier
  • Looker StudioFree tier

You never pay for a tool subscription. “Verified” means we checked the free access on the vendor’s own site this month. Tool names describe what you learn; no vendor endorses Launchpad.

03Syllabus

What you will learn, in order.

Lesson films are narrated with an AI voice, over visuals made with AI tools and checked by our team. Saarthi, the coach, is an AI. Mentors and reviewers are people.

Part 1 · The AI core, shared by every stream

00Set up your coach1 hLearn how the core works, write the learning-context document that tells every AI tool who you are, and set Saarthi up as a coach that makes you think instead of thinking for you.
  1. How this core works6 min
  2. Write your learning-context document8 min
  3. Seven roles for AI, and study modes7 min
01How LLMs work and fail2.5 hBuild a working mental model of a large language model: tokens, prediction, context windows and memory. Then learn its predictable failures (hallucination, sycophancy, stale knowledge, bad arithmetic) and reproduce them yourself.
  1. Tokens and next-word prediction7 min
  2. Context windows and memory7 min
  3. Hallucination, sycophancy and other failures8 min
  4. Lab: one prompt, three models, five failures8 min
02Description and context engineering3.5 hWrite briefs that get the right output the first time: role, task, context, examples and format. Then make them reusable with Claude and ChatGPT Projects, Gemini Gems, custom GPTs and NotebookLM, and learn to work with PDFs, spreadsheets and images.
  1. The brief: role, task, context, format8 min
  2. Examples and output formats7 min
  3. Projects in Claude and ChatGPT8 min
  4. Gems, custom GPTs and NotebookLM8 min
  5. Working with files: PDFs, spreadsheets, images8 min
03Verify everything2.5 hCheck AI output the way fact-checkers do: lateral reading, tracing citations and numbers to the original, using deep-research modes without trusting them blindly, and testing a prompt properly with a 10-case golden set.
  1. Lateral reading7 min
  2. Checking citations and numbers8 min
  3. Deep-research modes: use and limits7 min
  4. Your first eval: a 10-case golden set8 min
04Connectors and MCP2 hConnect AI to the places your work lives (Drive, Gmail, Sheets, business data) without writing code. Learn what an MCP server is, why a reviewed directory differs from unreviewed community servers, how to grant the least access that works, and how to keep browser extensions on a short leash.
  1. What connectors and MCP are8 min
  2. Trust, least privilege and the reviewed directory8 min
  3. Browser extensions and AI browsers7 min
  4. Lab: ask the seller-data MCP server, check every answer8 min
05Agents and automations3.5 hHand multi-step work to AI safely. Tell an agent from an automation, decide what to delegate, use browser and coding agents with care, and build an n8n workflow that uses AI with a human-approval step before anything leaves the system.
  1. Agents vs automations: what to delegate8 min
  2. Browser agents, safely7 min
  3. Automation basics with n8n and Zapier8 min
  4. Lab: build an automation with a human-approval step8 min
  5. Failure modes and your failure-mode note7 min
06AI for learning and careers1.5 hUse AI to learn faster without outsourcing the learning, write applications that still sound like you, and practise interviews out loud with Saarthi.
  1. Learning with AI, not instead of learning7 min
  2. Applications in your own voice7 min
  3. Mock interview with Saarthi8 min
07Diligence: law, ethics, integrity2.5 hThe rules that apply when you use AI in India: the DPDP Act 2023 and its 2025 Rules, academic integrity and disclosure, copyright (including where Indian law is still unsettled), and bias. You finish by writing your own AI-use policy.
  1. DPDP Act 2023: personal data and AI8 min
  2. Academic integrity and disclosure7 min
  3. Copyright and AI in India8 min
  4. Bias and fairness7 min
  5. Write your personal AI-use policy7 min
08Core capstone1 hPut all four Ds together on one anonymised marketplace settlement file: ask good questions, automate one step, verify every number against Saarthi's P&L engine, and defend your work live in five minutes.
  1. The capstone brief6 min
  2. Lab: ask, automate, verify8 min
  3. Prepare your 5-minute defence7 min

Part 2 · AI Guest Experience & Ops Automation

01The guest journey and where AI belongs2 lessonsMap every guest touchpoint, sort messages into intents, and decide which ones automation may touch.
  1. Mapping the guest journey from enquiry to review8 min
  2. Turn 200 guest messages into an intent map14 min
02Designing the concierge2 lessonsKnowledge base, conversation design, and the difference between a helpful concierge and one that makes things up.
  1. Concierge design: knowledge, flows and handoff9 min
  2. Build and test the concierge in the sandbox15 min
03Safety escalation: where automation stops2 lessonsAllergens, illness, safety and money: why a bot must never answer them alone, and how to prove yours doesn't.
  1. Allergens, illness and safety: the questions a bot never answers9 min
  2. Red-team your concierge14 min
04Multilingual service, done honestly2 lessonsServe guests in Kannada, Hindi and English without pretending a machine translation is fluent, and check it like a professional.
  1. Multilingual guests and the limits of machine translation8 min
  2. Build a translate-and-check loop13 min
05Ops automation: tickets and SLAs2 lessonsTurn housekeeping and maintenance requests into tickets with priorities and service levels, and automate the routing in n8n.
  1. Housekeeping and maintenance tickets: categories, priorities, SLAs8 min
  2. Build a ticket flow in n8n16 min
06Measuring service and handling guest data2 lessonsBuild an SLA dashboard managers actually use, and handle guest data the way India's DPDP Act expects.
  1. Guest data under DPDP, and measuring what matters9 min
  2. Build the SLA dashboard14 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Multilingual homestay concierge with allergen and safety escalation

    Tulasi Homestay, a synthetic six-room property near Udupi, wants guests answered day and night in English, Kannada and Hindi. Build a concierge in the Saarthi simulator that answers green intents from a maintained knowledge base, drafts amber replies for the owner, and routes every allergen, health, safety and money question to a human with fixed, native-checked messages. Prove it with tests and a red-team, then demo it live to the owner persona, who will try to make it answer an allergy question.

    You hand in

    • Intent map from 200 synthetic messages with colours and reasons
    • Knowledge base of at least 25 entries with owners, last-checked dates and native-checked translations
    • System prompt, fixed handoff texts and keyword list with verification notes
    • Test sheet (at least 30 functional cases) and red-team sheet (at least 40 attacks) with two full runs each
    • Half-page safety note for the owner
    • A 5-minute live demo and defence with Saarthi as the owner

    Data: Synthetic Tulasi Homestay message set (200 messages, multilingual, tokenised guest IDs) and KB facts from the Saarthi sandbox. No real guest data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    n8n housekeeping and maintenance ticket flow with an SLA dashboard

    Tulasi Grand, a synthetic 60-room sister hotel to Tulasi Homestay, loses track of guest requests across WhatsApp, the desk and phone. Build an n8n flow that turns every request into a classified, prioritised ticket with safety rules above the AI, routes it to the right person, escalates SLA breaches, and feeds a one-page dashboard the duty manager can act on each morning.

    You hand in

    • Exported n8n workflow with webhook trigger, safety rule, AI classification, validation, routing, approval queue and SLA watcher
    • Tickets sheet after at least 50 test messages, with a test log
    • SLA targets tab and a short rationale for each target
    • Dashboard (Looker Studio or Sheets) with four tiles, three charts, a bot panel and checked insight lines
    • A privacy and retention note for the sheet and logs

    Data: Synthetic 30-day ticket history and live test messages generated in the Saarthi sandbox for Tulasi Grand, a 60-room hotel. No real guest data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Anita D'Souza, owner of a six-room heritage homestay near Udupi who runs everything from her phone. She wants guests answered at 2 a.m. but has been burned once when a cousin's chatbot told a guest with a nut allergy that a dish was 'safe'. She tests whether you know where automation must stop.

On plans that include mock interviews. You get written feedback after every call.

06Enrol

Pick a plan for AI Guest Experience & Ops Automation.

One payment. The access deadline for each plan is shown before you pay.

  • Foundation

    01

    Learn the AI your field now runs on.

    ₹2,999

    One payment. GST included.

    • Access for 120 days (about 4 months) from the day you pay
    • No 1:1 mentor sessions
    • The common AI core and your domain course
    • Saarthi, your AI coach, any hour
    • Hands-on labs on free tools
    • Verifiable certificate
    Enrol on Foundation

  • Professional

    02

    Prove it on real work.

    ₹7,999

    One payment. GST included.

    • Access for 180 days (about 6 months) from the day you pay
    • One 1:1 session with a mentor
    • Everything in Foundation
    • Two live projects, reviewed by Saarthi and a mentor
    • Public portfolio page of verified work
    • Voice mock interviews
    • One 1:1 coaching session
    Enrol on Professional

  • Career

    03

    Work alongside our team.

    ₹14,999

    One payment. GST included.

    • Access for 240 days (about 8 months) from the day you pay
    • Four 1:1 sessions with a mentor
    • One 1:1 call with an industry expert
    • Everything in Professional
    • Four-week supervised practicum with the Saarthi team
    • Four 1:1 coaching sessions with your mentor
    • An exclusive 1:1 call with an industry expert in your field
    • CV and LinkedIn review
    • Priority mentor replies
    Enrol on Career

Your access period starts the day you pay and is shown before you pay. One exam pause per enrolment, for up to 15 days. When access ends the course locks, and your portfolio page and certificate stay live. Full refund within 7 days, pro-rata after that, and an automatic refund for any practicum, 1:1 mentor or expert session we do not deliver. Refund policy