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A3· Architecture and planning

Climate & Sustainability Analytics with AI

You read a site's climate from real weather data, test shading and ventilation in Ladybug, and write sustainability advice where every claim traces to a source you checked.

Choose a planRead the syllabus
Who it is for
BArch students from third year up and MPlan students. Ladybug labs need a Windows or Mac laptop with Rhino; the data and writing labs run in a browser. This course builds analytical 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 · 4.5 h
Live projects
2, on Professional and Career
01Roles it prepares you for
  • Sustainability Analyst
  • Building Performance Assistant
  • Junior Architect

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

02Tools you will use
  • Ladybug ToolsFree tierVerified
  • Rhino 8 (90-day evaluation or college lab licence)College licenceVerified
  • Grasshopper (ships inside Rhino)College licence
  • OneBuilding.org weather files (EPW)Free tier
  • Google Colab (free tier)Free tier
  • Deep-research mode in a free AI chatFree tier
  • 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 · Climate & Sustainability Analytics with AI

01Reading a climate2 lessonsWhat a weather file contains, what coastal Karnataka's climate demands, and how to check it yourself.
  1. Weather files and the warm-humid coast15 min
  2. Summarise a weather file with AI-written Python30 min
02Ladybug essentials2 lessonsThe core Ladybug charts, what question each answers, and how to read them without fooling yourself.
  1. Sun path, radiation, wind and comfort charts15 min
  2. Build a Ladybug climate dashboard30 min
03Shading and radiation for a coastal house2 lessonsSizing overhangs and roof protection with radiation analysis, for sun and for rain.
  1. Designing shade that works in sun and rain14 min
  2. Overhang depth study30 min
04Ventilation and comfort2 lessonsAdaptive comfort, air movement and when natural ventilation stops being enough in a humid climate.
  1. Comfort in a humid climate15 min
  2. Comfort hours with and without fans30 min
05Codes and standards with AI lookup2 lessonsUsing AI to find what NBC, ECBC and rating systems say, and verifying every clause yourself.
  1. The standards landscape and the citation trap15 min
  2. Build a sourced standards note30 min
06Writing a retrofit memo2 lessonsRanking retrofit options for an existing building with evidence, honest uncertainty and real costs.
  1. How to rank retrofit options14 min
  2. Draft a retrofit memo with a source log30 min
04Live projects

Two briefs. Real-shaped work.

  1. Project 1

    Ladybug climate analysis for a Mangaluru coastal house

    Design or take an existing concept for a two-storey family house on a coastal Mangaluru plot (you choose a plausible plot within a few kilometres of the coast, using public imagery for context). Using a documented Mangalore weather file, produce a full climate analysis in Ladybug: seasonal sun path, radiation on the massing, seasonal wind roses and adaptive comfort scenarios. Use the results to size shading on each facade, place openings for cross ventilation and protect walls from monsoon rain. Every chart states its period, hours and weather file; every design move links to a result.

    You hand in

    • Weather-file note: source, station, period, method, and why it suits the site
    • Ladybug definition (.gh) with sun path, radiation, seasonal wind roses and comfort scenarios
    • Shading study for at least the west and south facades with reduction-versus-depth plots
    • Ventilation and rain strategy shown on plan and section, tied to the wind roses
    • Comfort table across three seasons and three scenarios, with the residual discomfort stated
    • Six-page analysis report with captions, limits and an AI-use log

    Data: Public: Mangalore weather file from OneBuilding.org (ISHRAE or TMYx), and open satellite imagery for site context. The house design is the student's own.. Reviewed by Saarthi against a published rubric, signed off by a mentor.

  2. Project 2

    Retrofit options memo with verified sources

    An owner of an existing building in coastal Karnataka (the Example coastal house from the Saarthi sandbox, a synthetic small warehouse-with-office from the Saarthi sandbox, or a building you have permission to study) asks what to do first to make it cooler and cut energy use. Produce a ranked retrofit memo backed by your own analysis of at least two options, sourced costs and a standards note that quotes the relevant codes. Every number and requirement must trace to a source you opened; anything unverified is labelled as such.

    You hand in

    • Baseline and two modelled retrofit options with before-and-after results
    • Ranking table: effect, cost (sourced and dated), disruption, confidence, risks
    • Standards note quoting NBC 2016, ECBC or Eco Niwas Samhita as applicable, with clause, page and version
    • Two-page owner memo with ordered recommendations
    • Complete source log (claim, document, version, clause or URL, date checked, checked by)
    • Five-minute defence to Saarthi playing a building-physics reviewer

    Data: Public codes from BIS and BEE, a public weather file, supplier or schedule-of-rates prices, and either the course's example building or a synthetic warehouse profile from the Saarthi sandbox. No private occupant 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: Dr Rao, a building-physics consultant who reviews green-building submissions. She asks where every number came from, which weather file you used and why, and what your shading result would look like in a wet August instead of a dry April.

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

06Enrol

Pick a plan for Climate & Sustainability Analytics with 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