Use case
AI Copilot for Case Interviews
Consulting candidates (McKinsey, Bain, BCG, Tier-2 firms), MBA students preparing for case rounds, and PMs whose loops include consulting-style case questions.
Cases reward structure under time pressure
Case interviews are graded on the structure of your thinking, not the answer. Consultants want to see a clean issue tree, MECE branches, and a clear recommendation backed by numbers. Live, that's hard — you have 60 seconds to pick the right framework, three minutes to build the structure, and you can't go back and reorganize. Interview Cheat scaffolds the issue tree and runs the math out loud while you focus on judgment and communication.
How Interview Cheat helps in a live case
When the interviewer presents the case prompt, the overlay drafts a clean issue tree in the right framework — profitability (Revenue / Cost), market sizing (top-down or bottom-up), M&A (synergies / risks / valuation), growth strategy (Ansoff, BCG matrix, market entry).
Each branch of the tree has 2-4 MECE sub-branches with concrete questions to ask the interviewer next.
For market sizing, it lays out the assumption stack — population, penetration, frequency, average price — and tracks the running multiplication so you don't lose track.
For numerical questions, it shows the structure of the calculation step by step. You still do the arithmetic out loud (the interviewer wants to see it), but you never lose the place.
Recommendation lines at the end include the so-what, the risks, and the next step — exactly what case grading rubrics expect.
Features that matter for this
Right framework, picked fast
The drafted issue tree starts from the prompt and uses the canonical framework for that case type. You can rebrand it as your own — interviewers care about MECE coverage, not whose framework you cite.
Assumption-stack tracker for sizing
Population → penetration → frequency → conversion. The overlay holds each layer with a working number; you say it out loud and multiply on the whiteboard.
Numeric guardrail
If your running number is two orders of magnitude off the realistic range, you can see it on the panel before you say it out loud. Interviewers grade hard on order-of-magnitude sanity.
Recommendation closing line
Every case answer needs a so-what at the end: 'I would recommend X because Y, with the main risk being Z, and the immediate next step is W.' The overlay drafts that line for you.
Sample questions and how to answer them
A regional grocery chain has seen profits drop 15% over the last two years. What do you do?
Profitability framework: Profit = Revenue − Costs. Revenue = volume × price; segment by store, by category. Costs = COGS, labor, real estate, marketing. Identify the 1-2 biggest drivers, ask for data, recommend.
How many golf balls are sold in the United States each year?
Bottom-up market sizing. Population → percent who golf → games per year → balls per game (avg ~1.5 with loss factor) → replacement purchases. Sanity check against a top-down: golf is a $X billion industry, balls are Y%.
Our client makes premium kitchen knives and wants to enter India. Should they?
Market entry framework: market attractiveness (size, growth, competitive intensity), client capability (product fit, channel access, brand), entry mode (export, distributor, JV, own ops). Pick a recommendation with explicit risks.
A pharma client is considering acquiring a generic-drug manufacturer. Walk me through how you would think about it.
M&A framework: strategic fit, financial valuation (synergies + risks), execution risk (integration, regulatory). Lay out the structure first, then ask for the data you would need to drill into each branch.
Frequently asked questions
Does Interview Cheat work for MBB (McKinsey, Bain, BCG) case interviews?
Yes — the issue trees use the frameworks MBB interviewers calibrate to (profit decomposition, market sizing top-down/bottom-up, M&A synergies/risks). You deliver the structure in your own words; the overlay handles framework recall.
Will it do the math for me?
It tracks the assumption stack and shows running products, but you do the arithmetic out loud — the interviewer is grading your ability to sanity-check magnitudes in real time. The overlay catches order-of-magnitude errors before you say them.
Can I use it for digital / written cases?
For verbally-conducted live cases over Zoom or Google Meet, yes. For purely written case assessments with a typed-answer interface, the overlay is less useful — those are typically time-pressured but not real-time conversational.
Is using an AI overlay in a consulting case ethical?
Same question every interview tool faces. You still have to deliver the answer cleanly, hold the structure under interviewer pushback, and defend your numbers when challenged. The overlay helps with framework recall, not with the judgment the interviewer is actually grading.