Skip to content
Launchpad
Enrol

H1· Hospitality and tourism

Hospitality Revenue & Distribution AI

You will price rooms with a working model you built yourself, read where every booking comes from, and answer guest reviews in a voice the hotel is proud of.

Choose a planRead the syllabus
Who it is for
BSc Hotel Management students who want to work in revenue, reservations or sales, and anyone who has wondered why the same room costs a different amount every night.
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
  • Revenue Management Trainee
  • Reservations Coordinator
  • Sales Coordinator
  • Distribution Executive

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

02Tools you will use
  • Claude (free plan)Free tier
  • Google SheetsFree tier
  • ChatGPT (free plan)Free tier
  • OTA extranet simulator (Saarthi sandbox)Saarthi sandbox
  • Saarthi coachSaarthi sandbox

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 · Hospitality Revenue & Distribution AI

01The three numbers every hotel lives by2 lessonsOccupancy, ADR and RevPAR, how they trade off, and how to build a KPI sheet that AI checks but does not own.
  1. Occupancy, ADR and RevPAR, and why one alone lies9 min
  2. Build a 30-night KPI sheet and make Claude check it14 min
02Demand and dynamic pricing2 lessonsRead demand from pace, pickup, seasons and events, then build a transparent rule-based pricing model in Sheets.
  1. Reading demand: seasons, events, pace and pickup9 min
  2. Build a rule-based dynamic pricing model in Sheets15 min
03Distribution: OTAs, direct and the real cost of a booking2 lessonsHow OTAs, channel managers and rate parity work, and how to compare channels on what the hotel actually keeps.
  1. OTAs, direct bookings and channel cost9 min
  2. Model net revenue by channel and design a direct offer14 min
04Reviews as revenue data2 lessonsWhy review scores move bookings, how to tag reviews by theme and sentiment with AI, and how to check the tagging.
  1. What guests say: themes, sentiment and the score8 min
  2. Tag 60 reviews and measure how far to trust the model15 min
05Responses that sound like the hotel2 lessonsWrite review-response templates with AI, then audit them for tone, accuracy and privacy before anything is posted.
  1. Response templates that do not read like templates8 min
  2. Run a tone audit on AI-drafted replies14 min
06Guardrails and the weekly revenue brief2 lessonsWhen to override a model, what fair pricing looks like, and how to brief a GM every Monday with AI help and no invented numbers.
  1. When to override the model8 min
  2. Write the Monday revenue brief with AI, numbers first13 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Dynamic-pricing simulation for a Mangaluru boutique hotel

    Hotel Kadal, a synthetic 24-room boutique hotel in Mangaluru, prices by gut feel. Build a rule-based dynamic-pricing model for the next 90 nights, back-test it against last year's synthetic bookings, and simulate three scenarios: a verified local event weekend, a monsoon weekday stretch, and a sudden 10-room group enquiry. Recommend the floor, ceiling and rules the owner should adopt, and defend them in a live call with the owner persona.

    You hand in

    • Google Sheet with a settings tab, 90-night pricing model, floor, ceiling and override log
    • Back-test tab comparing model RevPAR with actual synthetic RevPAR for the same nights last year, with the method stated
    • Three scenario tabs with before and after prices and RevPAR
    • A demand calendar where every event date has a source link
    • A one-page recommendation for the owner (under 400 words) and a prompt log showing how AI was used
    • A 5-minute live defence with Saarthi playing the owner

    Data: Synthetic Hotel Kadal pack from the Saarthi sandbox: 365 nights of rooms available, sold and revenue by channel, plus a 90-night on-the-books snapshot. No real hotel or guest data.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    OTA review sentiment to response templates, with a tone audit

    Hotel Kadal has 300 synthetic OTA-style reviews and no reply policy. Tag every review by aspect and sentiment with a measured AI pipeline, report what each department should fix, build a response-template library with an escalation list, and audit AI-drafted replies for tone, truth and privacy.

    You hand in

    • Golden set of at least 30 hand-tagged reviews, model versions, agreement scores and a change log
    • Aspect × sentiment pivot with a one-page department action list
    • A library of 8 response skeletons, a voice card with banned phrases and a confirmed-fixes list
    • An escalation list and a holding-reply template for safety, legal and staff allegations
    • Tone audit of 20 AI-drafted replies scored on the seven-criterion rubric, with your rulings where a second model disagreed
    • A short reflection on where the model failed and what you changed

    Data: 300 synthetic, anonymised OTA-style reviews for Hotel Kadal from the Saarthi sandbox, including code-mixed and sarcastic examples. No real guest names or bookings.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Ramesh Shetty, owner of a 24-room boutique hotel near Mangaluru's Lighthouse Hill. He is sharp, sceptical of software, proud of his reviews and worried that the OTAs take too much. He interviews you for a revenue trainee seat and asks you to defend tonight's rate.

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

06Enrol

Pick a plan for Hospitality Revenue & Distribution AI.

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