Skip to content
Launchpad
Enrol

B3· Business, management and commerce

AI for E-commerce & Marketplace Operations

You will run the operations side of a marketplace seller, from catalogue to returns to reimbursement claims, with AI agents connected to the seller's data, and you will catch money and problems a busy seller misses.

Choose a planRead the syllabus
Who it is for
BBA, BCom and MBA students who want to work with Amazon and Flipkart sellers, at a brand, an agency or a marketplace's seller-success team.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 4.6 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Marketplace executive
  • Catalogue executive
  • Seller-success associate
  • Account manager at an e-commerce agency

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

02Tools you will use
  • Saarthi MCP server over the anonymised seller data packSaarthi sandbox
  • Claude (free plan)Free tier
  • ChatGPT (free plan)Free tier
  • Gemini (free plan)Free tier
  • n8n (Saarthi-hosted sandbox)Saarthi sandbox
  • Google SheetsFree tier
  • Looker StudioFree tier
  • Canva (free plan, Canva AI features)Free tier
  • Saarthi anonymised seller data packSaarthi 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 E-commerce & Marketplace Operations

01How a marketplace account runs2 lessonsFulfilment models, the reports that describe an account, the metrics that can get it suspended, and connecting an AI assistant to the data safely.
  1. Fulfilment models, reports and account health12 min
  2. Question an account through MCP30 min
02Catalogue operations at scale2 lessonsBulk listing with templates, variation families and compliance attributes, generated with AI and validated before upload.
  1. Bulk listings without bulk mistakes10 min
  2. Fill and validate a bulk template35 min
03Returns and RTO2 lessonsThe difference between customer returns and return-to-origin, how to find root causes by reason, SKU, size and pincode, and how AI clusters free-text comments.
  1. Why orders come back10 min
  2. A returns root-cause dashboard35 min
04Reimbursements and SAFE-T claims2 lessonsWhich losses a seller can claim, the filing windows, the evidence each claim needs, and drafting claims for a seller to review. You draft; you never file.
  1. Money a marketplace may owe a seller14 min
  2. A claim finder that drafts, never files40 min
05Inventory planning and alerts2 lessonsDays of cover, reorder points and safety stock for marketplace sellers, and n8n alerts that tell a person when to act.
  1. Never out of stock, never drowning in it10 min
  2. An n8n reorder and price alert35 min
06Account health and the weekly business review2 lessonsKeeping an account safe, writing to sellers and marketplace support clearly, and running a weekly review that turns data into decisions.
  1. Protecting the account and talking to people10 min
  2. Run a weekly business review with Saarthi as the client35 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    SAFE-T and reimbursement finder: flag eligible claims and draft them, never file

    Find every case in the sandbox seller's anonymised data where Amazon may owe money: FBA units lost or damaged in a fulfilment centre, refunds where the return never came back, and SAFE-T-eligible seller-fulfilled returns. Apply written eligibility rules and filing windows, dedupe against existing reimbursements and claims, value each case from the data and draft claim notes for the seller to review. You draft; you never file. Your case list and values are checked against the frozen output of Saarthi's SAFE-T and reimbursement engine on the same dataset version.

    You hand in

    • Eligibility rules sheet with each window, its start date and the source or assumption behind it
    • Case register: id, type, ASIN or SKU, event date, last day to file, status (open, closing soon, missed), value
    • Recoverable total for open cases only, with missed cases listed separately
    • Draft claim notes for the 15 highest-value open cases, each with an evidence list and 'DRAFT - not filed'
    • Engine comparison with every difference explained
    • A one-page seller summary and a note on what you would automate and what must stay human

    Built on Saarthi’s engines. Your key numbers are checked against Saarthi's own SAFE-T and reimbursement logic, run on the same anonymised data.

    Data: Saarthi anonymised seller data pack, frozen version: a synthetic apparel-and-home seller on Amazon.in and Flipkart with hashed order ids, invented SKUs and brand names, and no buyer names, phones or addresses. This project uses the inventory ledger, reimbursements, returns, refunds, settlements and SAFE-T claim reports.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Returns and RTO root-cause dashboard with an automated reorder alert

    The sandbox seller's returns are rising and two bestsellers stocked out last month. Build a one-page returns and RTO root-cause dashboard with an action list, and an n8n workflow that alerts a person when SKUs cross their reorder point, with suggested quantities and a human approval step. Present both to Saarthi playing the agency head.

    You hand in

    • Prepared returns dataset with customer-return versus RTO classification and actual return cost
    • One-page dashboard (Looker Studio or Sheets) aggregated to region level, with a minimum-orders rule
    • AI-clustered comment themes for the worst SKUs with a hand-checked count
    • Top-five action list with evidence, owner and success measure
    • n8n reorder workflow JSON with settings and state sheets, approval step and test screenshots
    • A 5-minute client presentation

    Data: Saarthi anonymised seller data pack, frozen version: a synthetic apparel-and-home seller on Amazon.in and Flipkart with hashed order ids, invented SKUs and brand names, and no buyer names, phones or addresses. This project uses returns, orders, settlements, inventory and 90 days of sales history.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

05Practise out loud

In voice mock interviews and client calls, Saarthi plays: Ananya Rao (fictional), head of seller success at a Bengaluru agency that runs about forty Amazon and Flipkart accounts. She is short of people before the festive season. She gives you an account's reports and asks what is broken, what money is recoverable, and what you would automate first, and she pushes back on anything you cannot show in the data.

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

06Enrol

Pick a plan for AI for E-commerce & Marketplace Operations.

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