Skip to content
Launchpad
Enrol

F3· Fashion and textile

AI E-commerce Catalogue & Virtual Try-on

You will turn flat-lay and ghost-mannequin photos into a marketplace-ready, honestly labelled catalogue, with listings that pass a compliance check and help cut size returns.

Choose a planRead the syllabus
Who it is for
BDes Fashion students who want to run the catalogue desk of a D2C or marketplace fashion brand, from shoot list to live listing.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 6.6 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Catalogue / E-com Content Executive
  • Visual Merchandiser (digital)
  • Catalogue QC Associate

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

02Tools you will use
  • PhotoroomFree tier
  • Adobe FireflyFree tierVerified
  • AlphaBakeSaarthi sandbox
  • PICTOFiTSaarthi sandbox
  • ZMO.aiSaarthi sandbox
  • BotikaCollege licence
  • ClaudeFree tier
  • ChatGPTFree tier
  • Google SheetsFree 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 E-commerce Catalogue & Virtual Try-on

01The catalogue desk and its image standards2 lessonsWhat a catalogue team actually ships, how marketplace image rules work, and how to build a shot list that survives QC.
  1. What a listing is made of, and why images fail QC25 min
  2. Lab: audit a catalogue and write a shot list with AI35 min
02Clean-up, ghost mannequin and colour accuracy2 lessonsBackground removal, ghost-mannequin composites and the colour checks that stop 'not as shown' returns.
  1. Colour you can trust: references, drift and QC25 min
  2. Lab: clean up 6 SKUs and log colour drift40 min
03AI models and virtual try-on, honestly2 lessonsHow AI model generation and try-on work, where they mislead, and how to disclose and check them.
  1. AI models, try-on and the disclosure rules30 min
  2. Lab: generate and fit-check AI model shots40 min
04Attributes, titles and bullet points2 lessonsFilling structured attributes and writing titles and bullets with an LLM, then validating every field against the product.
  1. Attributes are how customers find you25 min
  2. Lab: fill and validate attributes for 10 SKUs40 min
05Size charts and the content that cuts returns2 lessonsGarment versus body measurements, building size charts in cm and inches, and fit notes that reduce size returns.
  1. Size charts that tell the truth30 min
  2. Lab: build and stress-test a size chart35 min
06Compliance, QC sampling and going live2 lessonsMandatory declarations on Indian marketplaces, a QC sampling plan for AI output, and a launch checklist you can defend.
  1. The declarations every fashion listing needs30 min
  2. Lab: compliance check and QC sample before launch40 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Ghost-mannequin to AI-model catalogue for 20 SKUs

    A synthetic ethnic-wear D2C brand has ghost-mannequin and flat-lay photos for 20 new SKUs and no budget for a model shoot. Produce a marketplace-ready image set with AI-generated model images, and prove it is honest. Every published image must pass a colour check against a white-balanced master and a fit-and-truth check against the spec sheet, and every AI model image must carry a clear disclosure with model size and garment length. Where an SKU fails twice, recommend a real shoot instead of forcing it. You present the set to the brand's catalogue head (Saarthi plays her) and defend three images she picks at random.

    You hand in

    • Final image set for 20 SKUs (primary, back, detail, AI model) in the dated panel frame
    • Colour QC log with two samples per image versus master and pass/flag results
    • Fit-and-truth checklist per AI model image with a verdict
    • Disclosure line per SKU and confirmation that Content Credentials are kept
    • Reshoot list with reasons for SKUs that failed
    • A 5-minute recorded walkthrough defending three randomly chosen images

    Data: Saarthi-provided photo set of 20 synthetic or anonymised SKUs (raw shots with grey card, inside/outside neck pairs, detail shots) plus a synthetic spec sheet with fabric, measurements, sheerness and included items. When available: until the real shoot is uploaded, the pack holds the shot manifest (fash_f3_photo_manifest.csv), the spec sheets and placeholder raw drawings for six SKUs, and the sandbox try-on and ghost-mannequin tools are not built yet; fash_f3_tryon_inputs.csv sets out what to feed them.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Myntra/Amazon listing build with size chart and compliance checklist

    Build complete listings for 10 SKUs of a synthetic women's ethnic-wear brand in two marketplace formats (Myntra-style and Amazon-style), using the dated attribute and style-guide exports in the sandbox. Use an LLM to draft attributes, titles and bullets, and validate every field against the allowed values and the brand's product master. Build garment-measurement size charts in inches and centimetres with fit notes drawn from synthetic return comments. Complete a compliance checklist for each SKU covering country of origin, manufacturer or importer details, MRP inclusive of taxes, net quantity, generic name, wash care and unprovable claims. Finish with a sampled QC and a signed go-live note.

    You hand in

    • Two flat-file listing sheets (Myntra-style and Amazon-style) for 10 SKUs
    • Validation sheet showing allowed-value and source checks for every attribute
    • Size charts per fit with grading checks and fit notes linked to return themes
    • Compliance checklist per SKU with document references
    • A note with your measured LLM field error rate and how you fixed it
    • Signed go-live note with sample, failures and decision

    Data: Synthetic: a product master for 10 SKUs, dated attribute dropdown and style-guide exports in marketplace-style formats, measured garment specs, and 60 synthetic return comments. No real seller 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: Priya, catalogue head at a Bengaluru ethnic-wear D2C brand that sells on its own site, Myntra and Amazon. She has just had 40 listings pulled for colour complaints, she distrusts AI model shots, and she will ask you to defend every image, attribute and size-chart number you show her.

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

06Enrol

Pick a plan for AI E-commerce Catalogue & Virtual Try-on.

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