Abstract: In late 2026, Meera Iyer, Head of Growth at Kalpaka AI Labs, a Noida-based AI-native startup, must present a quarterly search engine optimisation (SEO) plan to her board. Kalpaka sells agentic customer support, document AI, an analytics copilot and a developer platform, yet competitors dominate Google results and AI Overviews. The CFO has capped SEO spending at ₹14 lakh a quarter, while an agency proposes ₹32.4 lakh. Meera must choose between fixing the website’s technical foundations and content first, buying authority through digital PR and partnerships, hiring the agency, or blending the options. Each choice trades short-term visibility against long-term compounding and must survive the CFO’s demand for a twelve-month payback. The case asks students to allocate a constrained budget across on-site and off-site levers, prioritise among product lines with different search landscapes, judge the risks of scaled AI content, and define metrics for success when generative search is reshaping clicks. It suits courses in digital marketing, analytics and strategy. This case study has an active simulation to ensure learning while engaging.
Download the case study here: Kalpaka_AI_SEO_Budget_Case
The case study sets up the context for the gaming simulation. Start off the simulation after you have read the case study.
START THE CASE SIMULATION
Teaching objectives
- Introduce SEO as a capital-allocation decision rather than a purely technical task, where a startup’s limited resources force trade-offs.
- Distinguish on-site levers (technical health, content, structured data, programmatic pages) from off-site levers (digital PR, partnerships, reviews, community), and show how they depend on each other.
- Show how generative search features such as AI Overviews and AI Mode change visibility, click-through and the metrics marketers should track.
- Show that one search strategy does not fit a multi-product portfolio, because search demand, competition and contract value differ by product.
- Frame the ethical and policy limits of search marketing, including scaled AI content and manipulative link building, and their consequences for reputation and investors.
- Develop students’ ability to defend a recommendation to senior stakeholders whose goals conflict: a frugal CFO, an engineering-first CTO and a growth-focused CEO.
Learning outcomes
By the end of the session, students should be able to:
- Analyse the case data (keyword difficulty, search volume, CPC, Domain Rating, referring domains, AI Overview citation share) to diagnose Kalpaka’s search visibility gaps against competitors.
- Evaluate the three budget options and a hybrid, comparing cost, speed of impact, risk and how well each compounds over time.
- Construct a quarterly SEO budget of ₹14 lakh or less, splitting on-site and off-site spend and justifying which products to prioritise.
- Compare organic search with paid search, events and referrals using cost per SQL and win rates, and explain why organic returns lag spend.
- Design a measurement framework that combines leading indicators (indexing, rankings, AI-answer citations, referring domains) with business outcomes (SQLs, pipeline, payback).
- Assess the risks of shortcuts such as unreviewed AI-generated pages, and set guidelines for using AI in content production within search engine policies.
- Communicate a board-ready recommendation that answers the CFO’s payback, measurement and “why not generate 500 pages” questions.
The simulation extends outcomes 3 to 6 over six quarters by adding search ads and the consequences of black-hat tactics. I can add these sections to the Word case or turn them into a full teaching note with discussion questions and a suggested board plan.