# Build brief — a focused alternative to Final Round AI

> **Verdict:** Not faithfully · **Buildability:** 6/100 · **Category:** Career
> **Source:** https://www.canitbevibecoded.com/final-round-ai
> Independent editorial assessment from Can It Be Vibe Coded? Not affiliated with, endorsed by, or derived from Final Round AI. Verify current pricing and capabilities before acting.

## Context

**Final Round AI** — Provide ethical offline interview practice and post-session review without live deceptive assistance. It currently costs $99/mo.

A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Final Round AI, provide ethical offline interview practice and post-session review without live deceptive assistance. The hard boundary is live transcription, proprietary coaching, interview data, integrations, and real-time infrastructure, plus data, distribution, and coaching.

This brief describes a focused, single-operator replacement for the part of Final Round AI that is genuinely reproducible. It is deliberately narrower than the product it replaces, and it says so in writing. Build the useful core; do not pretend to have rebuilt the rest.

## What you are building

Run ethical offline interview practice with post-session review, tailor user-authored materials against a supplied role, and keep every claim traceable to the user's own evidence, without live deceptive assistance.

- Build a focused single-user workflow with real persistence, search, and export.
- A responsive interface with real empty, loading, success, and error states.

## Requirements

### Functional

- Local browser.
- Resume and job-description files.

### Data and integrations

- Optional OpenAI API key.

Each of these needs a real account, credential, or quota. Set them up before writing feature code.

### Non-functional

- Accessibility: semantic markup, labelled controls, visible focus, and reduced-motion support.
- Security: server-side secrets, validated input, and no credentials in the client bundle.
- Reliability: retries with backoff on external calls, and a clear failure state when a provider is down.
- Portability: the operator can export their data and leave without losing it.

## Implementation brief

Build a closest honest personal substitute for Final Round AI in an empty repository.
Use Next.js 15, TypeScript, SQLite, Drizzle ORM, and an optional OpenAI API; do not offer alternative stacks.
The core loop is: run ethical offline interview practice with post-session review, tailor user-authored materials against a supplied role, and keep every claim traceable to the user's own evidence, without live deceptive assistance.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Create a structured evidence bank for roles, projects, skills, dates, metrics, and source notes.
Import a job description and highlight requirements without inventing missing experience.
Generate a tailored resume variant only from approved evidence and show the source for each bullet.
Add application stages, contacts, tasks, dates, notes, documents, and a follow-up view.
Provide interview-question practice with answer notes and a self-review rubric, not deceptive live assistance.
Export resume data as JSON and PDF plus the application tracker as CSV.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out automatic mass application.
Deliberately leave out fabricated qualifications or deceptive interview assistance.
Deliberately leave out proprietary recruiter databases and guaranteed job outcomes.
Finish by running the tests and listing the exact commands used.

## Delivery standard

- Inspect the repository first, then write a short implementation plan before writing code.
- Deliver the smallest complete end-to-end workflow first; every primary control must work against persisted data.
- Use real validation and storage; never substitute fake dashboards, decorative controls, hard-coded success states, or mock integrations.
- Include responsive layouts plus genuine empty, loading, success, validation, and failure states.
- Keep secrets server-side in environment variables, provide .env.example, and never commit credentials or user data.
- Add structured logs around every external call and return actionable errors without leaking sensitive details.
- Write unit tests for the core logic and one automated test of the main user journey.
- Finish with a README covering setup, architecture, data location, backups, tests, deployment, and known limitations.

## Acceptance criteria

- [ ] A clean install starts the app using only the README and .env.example.
- [ ] The primary journey works from first visit through saved result, reload, edit, export, and deletion where applicable.
- [ ] Invalid input, missing configuration, provider failure, and an empty database each have a usable state.
- [ ] The interface works at 390px and 1440px, is keyboard navigable, and shows visible focus on every control.
- [ ] Tests, type checking, linting, and a production build all pass with no ignored failures.
- [ ] No part of the interface implies a live integration, security guarantee, or scale capability that was not actually built and verified.

## Non-goals

Do not build these, and do not claim to have replaced them:

- Live transcription, proprietary coaching, interview data, integrations, and real-time infrastructure.
- Proprietary recruiter data.
- Job-board distribution.
- Human coaching.
- The useful dataset is owned, accumulated, or expensive to reproduce.
- Licensed content and distribution rights are not reproducible with an LLM.

## What you still own after launch

- Secure credentials, rotate secrets, and handle provider rate limits.
- Run migrations, backups, restores, and dependency updates.
- Test the critical journey after every model, API, or hosting change.
- Monitor failures and fix the edge cases a first prompt will miss.
- Maintain every third-party integration as APIs and OAuth rules change.

## Risk

**Operational risk.** The code is achievable; dependable data, integrations, and ongoing operations are the real cost.

Editorial confidence in this assessment: high. No independent one-shot implementation is linked yet.

## Prior art

Working open-source software you can read, fork, or borrow from before starting:

- [Reactive Resume](https://github.com/AmruthPillai/Reactive-Resume) — Active open-source resume builder with structured data and PDF export

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Generated by [Can It Be Vibe Coded?](https://www.canitbevibecoded.com) · Full report: https://www.canitbevibecoded.com/final-round-ai