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A2· Architecture and planning

AI Visualisation & Presentation

You produce client-ready architectural images and decks with AI renderers in hours instead of days, and you can point to every place an image shows something that cannot be built.

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Who it is for
BArch and BDes students who can model a building in SketchUp, Rhino or Revit. A laptop is needed for the rendering labs; prompting and critique work on a phone. This course builds visualisation skill; it does not replace the Council of Architecture's mandatory practical training in a registered architect's office.
AI core
9 units · 20 h
This course
6 modules · 12 lessons · 4.2 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Architectural Visualiser
  • BIM/Presentation Associate
  • Design Presentation Assistant

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

02Tools you will use
  • D5 Render (Education or Community)Free tierVerified
  • Veras (15-day free trial; paid after)College licenceVerified
  • LookX AI (free credits)Free tier
  • Saarthi sandbox image modelSaarthi sandbox
  • Claude, ChatGPT or Gemini (free tier)Free 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 Visualisation & Presentation

01What AI renderers actually do2 lessonsHow diffusion-based renderers turn a model or sketch into an image, and why they move your windows.
  1. Diffusion, guidance and geometry drift14 min
  2. One massing, three renderers30 min
02Prompting for architecture2 lessonsWriting prompts that describe a real place, material and camera, and building a reusable prompt library.
  1. Anatomy of an architectural prompt14 min
  2. Build a prompt library for one project25 min
03Real-time rendering with AI assistance2 lessonsWhen to use a physically based renderer like D5, and how its AI features speed up the boring parts.
  1. Physical rendering versus generation13 min
  2. A D5 scene with honest light30 min
04Finding what is not buildable2 lessonsA structured red-pen review of AI images, and how to label them so nobody is misled.
  1. The not-buildable checklist15 min
  2. Red-pen audit of ten images30 min
05Presentations that persuade2 lessonsTurning images into a deck a client decides on, with AI drafting and you editing.
  1. Structure of a client pitch14 min
  2. Build a six-slide pitch30 min
06Ethics, IP and disclosure2 lessonsWho owns AI images, what tool terms allow, and how to caption a portfolio honestly.
  1. Ownership, terms and honesty14 min
  2. Write your disclosure and captions20 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    AI-rendered presentation set versus a traditional one

    Take one of your own studio designs (or the course's example coastal house model) and produce two presentation sets of the same five views: one with a traditional workflow (physically based rendering and conventional post-production) and one with an AI-assisted workflow (AI renderers and AI features). Log the time of every step in both. Audit the AI set for geometry drift against the model and for anything not buildable, and report honestly what the AI workflow saved, what it cost in checking and fixing, and which images you would actually show a client.

    You hand in

    • Traditional set: five views rendered from the model, with render settings noted
    • AI-assisted set: the same five views, with prompts, tools and settings logged in a prompt library
    • Drift overlays for every AI image against the model or base render
    • Not-buildable log: every issue numbered, categorised and resolved (redesign, re-render or label)
    • Time log for both workflows, step by step, including audit and fix time
    • Two-page findings note with captions, labels and your recommendation

    Data: The student's own 3D model, or the Saarthi example coastal-house model. No client or third-party images.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Client pitch deck for a D2C warehouse-cum-store

    A synthetic D2C home-goods seller in Mangaluru (profile in the Saarthi sandbox) needs a warehouse with a small walk-in store and customer parking on a plot you define. Design a concept massing and layout, then build a 10- to 12-slide pitch deck that helps the owner decide between two options. Use AI rendering where it helps, keep every image drift-checked and labelled, and leave every cost or timeline either sourced or marked TO FILL. Present it live to Saarthi playing the owner, then revise.

    You hand in

    • Concept model with two options and a goods-flow and customer-flow diagram
    • 10 to 12 slide deck with message headlines, labelled images and image notes
    • A sources sheet for every number, or TO FILL markers where real rates are needed
    • Recording or transcript of the rehearsal with Saarthi as the client, and the revisions made
    • Disclosure pack: studio AI policy, captions and terms sheet

    Data: Saarthi sandbox: a synthetic seller profile (order volumes, product mix, staff count). Plot dimensions chosen by the student. 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: Mr Shenoy, owner of a growing Mangaluru home-goods brand who wants a warehouse with a small walk-in store. He is busy, asks what the building will cost before he looks at the pictures, and gets suspicious when an image looks too perfect.

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

06Enrol

Pick a plan for AI Visualisation & Presentation.

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