Learning and careers decision

Road to offer

A technically skilled developer can build a useful subset (guided cases, voice transcription, LLM debriefs, drills) in a few months, but reproducing the product's content volume, analytics, and brand-driven trust is expensive and time-consuming.

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You pay

Not priced

No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.

You’d pay instead

$100one-off120 h to build

$200/mo6 h/mo upkeep

No published price to break even against.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Student runs a guided case or voice case → system records answers and timers → backend scores and generates an AI debrief → store progress and recommend drills

What it still won’t have

  • Large production case & drill bank and curated firm-specific content
  • Polished UX and product polish (voice-mode reliability, debrief presentation)
  • Proprietary platform analytics and published outcome signals
  • Brand recognition and existing user base/trust

What remains hard

  • Brand trustTrusted by 2,147+ candidates
Read the build prompt

First-year cost

No published price

Road to offer does not publish a price we could read, so there is nothing to compare against. What building costs is below.

Money you would actually spend

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

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What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a self-hosted consulting case interview practice app using Next.js + React frontend, Postgres + Prisma backend on a single DigitalOcean droplet (or Render) with Stripe for subscriptions, OpenAI (or preferred LLM) for AI debriefs, and a speech-to-text API (OpenAI Whisper or AssemblyAI) for voice case transcription. In scope: guided case flow with step timing and state save, voice recording widget + upload + transcription pipeline, LLM-based debrief generation returning a 7-category scorecard and textual feedback, timed drill engine with instant scoring, user accounts and progress storage, subscription checkout and access control, admin import for case/drill content, basic analytics dashboard, input validation, error handling, and unit/integration tests for core flows. Out of scope: training proprietary models, large-scale multi-tenant infra, and a full 600+ drill content import (seed with 40 guided cases and 100 drills). Include logging, retry/backoff for external APIs, secure storage of recordings, and CI tests.
How we checked3 sources · 3/3 runs agreed · evidence score 59

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • 3/3 assessment runs agreed+4
  • Evidence score59

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ 3 independent runs, one answer✓ Citations limited to fetched pages! 1 moat quoted from the page