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H3· Hospitality and tourism

AI for F&B, Culinary & Cloud-kitchen D2C

You will know which dishes actually make money after delivery-app costs, photograph them honestly, and put a packaged product on sale with a label you can defend.

Choose a planRead the syllabus
Who it is for
Culinary Arts and Hotel Management students who can cook or run a pass, and want to understand the numbers and the listing behind a food business.
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
  • F&B Management Trainee
  • Cloud-kitchen Operations Executive
  • Menu and Costing Assistant
  • Food D2C Listing 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
  • Canva (free plan) photo editingFree tier
  • SnapseedFree tier
  • Gemini (free)Free tier
  • Cloud-kitchen orders and payout pack (Saarthi sandbox)Saarthi 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 · AI for F&B, Culinary & Cloud-kitchen D2C

01Recipe costing2 lessonsFood cost, yield and contribution margin, built from a standard recipe card that AI checks but does not invent.
  1. Food cost, yield and contribution margin9 min
  2. Cost five recipe cards with AI as your checker14 min
02Menu engineering2 lessonsClassify every dish by popularity and contribution, then decide what to promote, reprice, rework or remove.
  1. Stars, plowhorses, puzzles and dogs8 min
  2. Build the menu-engineering matrix in Sheets14 min
03Cloud-kitchen item P&L2 lessonsWhat a delivery-app order really pays the kitchen, and how to build an item-level P&L from a payout statement.
  1. From menu price to money in the bank9 min
  2. Build an item P&L from a payout file and check it against Saarthi16 min
04AI food photography, honestly2 lessonsShoot real food well on a phone, use AI to edit within honest limits, and know where enhancement becomes misrepresentation.
  1. Enhancement versus misrepresentation8 min
  2. Shoot, edit and approve five dish photos15 min
05Packaged food D2C and FSSAI labelling basics2 lessonsWhat a packaged-food label must carry, which claims need care, and how to use AI to check a listing without trusting it as the law.
  1. FSSAI labelling basics for a packaged product10 min
  2. Run a label-claims check on a D2C listing15 min
06Prep forecasting and waste2 lessonsForecast tomorrow's prep from order history, log waste, and use AI to find patterns without trusting it to predict alone.
  1. Forecasting prep and cutting waste8 min
  2. Build a morning prep sheet from eight weeks of orders14 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Menu engineering, AI food photography and item-level P&L for a cloud kitchen on Swiggy/Zomato

    Kudla Bowl, a synthetic Mangaluru cloud kitchen selling on food-delivery apps, has rising orders and flat profit. Using the versioned sandbox pack, cost every item, build the item-level P&L after discounts, platform deductions and packaging, re-run menu engineering on delivery contribution, and produce honest new photos and descriptions for the puzzles. Your contribution figures are checked against Saarthi's P&L engine on the same dataset version. Recommend what to keep, reprice, rework and remove, and defend it to the owner.

    You hand in

    • Costing workbook with yield-based edible costs and dated prices
    • Item-level P&L on the pack's reference plate costs, with dataset version, reconciliation to statement totals, an engine comparison table and a column comparing your own costing
    • Menu-engineering matrices on dining and delivery contribution, with the differences explained
    • At least four original and edited photo pairs with an approval sheet, plus honest descriptions for puzzle items
    • A one-page owner recommendation with a four-week measurement plan
    • A 5-minute live defence with Saarthi as the owner

    Data: Versioned synthetic cloud-kitchen pack from the Saarthi sandbox: 8 weeks of orders, a payout statement with illustrative deduction terms, recipes, prices, and Saarthi's reference plate and packaging costs (the shared basis for the engine check; the student's own costing sits in a comparison column). Photos are the student's own of real food.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Packaged-food D2C listing with an FSSAI label-claims check

    Kudla Bowl wants to sell a packaged product online. Choose one synthetic product from the sandbox (masala, pickle or snack), prepare its label text from the recipe and lab report, write a marketplace or D2C listing, and run a structured claims check against the current FSSAI Labelling and Display and Advertising and Claims texts. Show where AI helped, where it was wrong, and what still needs an expert.

    You hand in

    • Label checklist with each required element, your text, the regulation section or FSSAI page relied on, and the date checked
    • Ingredients list in descending order of weight with allergens identified from the recipe
    • Claims decision table for every claim-like phrase in the founder's draft and your listing
    • A final listing under 150 words with a line-by-line consistency check against the label
    • A log of AI flags marked correct, wrong or uncertain, with the source you used to decide
    • Three questions for a food-regulatory consultant

    Data: Synthetic product pack from the Saarthi sandbox: recipe by weight, synthetic nutrition lab report, synthetic licence number and a deliberately flawed founder draft listing.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Faiz Khan, who runs two delivery-only kitchens in Mangaluru selling ghee roast, neer dosa combos and biryani bowls on both big food-delivery apps. Orders are up, money in the bank is not, and he wants to know which dishes to kill. He tests whether you can explain his payout statement line by line.

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

06Enrol

Pick a plan for AI for F&B, Culinary & Cloud-kitchen D2C.

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