MyConsultingCoach.com is a data-driven coaching platform for people applying to management consulting, and it's still running today. It takes candidates end to end: from CV review to content, coaching, and the workflows that hold it all together. I started it before AI was a thing.
I founded it, built it, and ran it across product, engineering, and operations as a bootstrapped business that has been profitable for years. This case study covers v1, the original platform that proved the model; the product has since been rebuilt on a new codebase.
Candidates want offers from McKinsey, BCG, and Bain. The bar is high, the process is opaque, and most people go in underprepared. We exist to take someone from "I want to apply" to "I'm ready," with structure, real coaching, and material that actually reflects how the interviews work.
Candidates wanted in, but the preparation resources available to them were broken in five predictable ways:
A platform that owned the whole candidate journey, the full path from first visit to final offer.
Rather than a small high-touch consultancy, I built a platform that could serve candidates at scale while keeping quality consistent, the only way to address scarcity and gatekeeping at once.
The obvious move was an open coach marketplace. I deliberately chose a structured mentoring service instead: a marketplace optimizes for liquidity, a mentoring service optimizes for outcomes. The structure was the product; it let us guarantee a quality bar a free-for-all never could.
The library was the heart of the product and the acquisition engine. Great content earned trust, drove organic growth, and gave candidates a reason to stay before they ever paid.
Preparation is lonely and long. Building community surfaces like the meeting board kept candidates engaged and practicing together between coaching sessions.
v1 was a Laravel and Vue application that grew into a substantial product over years: content management, coaching and scheduling, payments, community features, dashboards, and the admin tooling needed to operate a real business. The business still runs today, on a newer codebase that succeeded it.
The harder engineering was longevity: keeping a complex, multi-surface product maintainable, reliable, and profitable through a decade of feature growth and changing requirements, with a small team.
Commercial proof, without quoting revenue: a bootstrapped product that found its market, kept its customers, and has run profitably for years, built and grown largely through organic acquisition. The business is still operating today, now on a rebuilt codebase.