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    Career Technology · AI

    Gradr

    An AI-powered career accelerator designed to help students, graduates, and early-career professionals build stronger resumes, prepare for interviews, and manage their career journey.

    Previously known as CareerFlow OS.

    Built with LovableAIReact

    Timeline

    When this piece of work started and where it stands today.

    1. Started

      1 Sept 2025

    2. Status

      Building — in progress

    Problem

    Early-career candidates get almost no useful feedback. Resumes disappear into applicant tracking systems without explanation, interview practice is either expensive or unstructured, and applications end up scattered across spreadsheets, inboxes and browser tabs. The missing piece is not more advice — it is a system that reviews your material, tells you exactly what is weak, and keeps the whole search in one place.

    What I built

    • AI resume analysis
    • ATS scoring
    • Job tracking
    • AI mock interviews
    • Interview analytics
    • Career development tools

    Built by Andrew

    Tech stack

    • Interface

      ReactTypeScriptTailwind CSSComponent library

    • AI layer

      LLM APIsStructured promptingScoring rubricsStreaming responses

    • Backend

      PostgresRow-level securityAuthFile storageServer functions

    • Platform

      LovableMCP integrationCredit-based usage metering

    Challenges & how I solved them

    • Making AI feedback specific, not generic

      The first versions returned polite but useless advice. I moved from open-ended prompts to fixed scoring rubrics with structured output, so every response has to point at a real line in the resume or answer and explain what to change.

    • Approximating ATS behaviour honestly

      Real applicant tracking systems are closed. Instead of pretending to replicate one, the score is built from things that are actually checkable — parseability, section structure, keyword coverage against the job description and formatting risks — and the app explains each component.

    • Keeping AI cost predictable

      Long resumes and interview transcripts get expensive fast. Requests are trimmed and cached, heavy analysis runs on demand rather than on every keystroke, and usage is metered with a credit balance so cost stays bounded per user.

    • Data privacy on personal documents

      Resumes are sensitive. Everything is scoped per user with row-level security, files live in access-controlled storage, and no document is readable across accounts.

    Early traction

    Gradr attracted 300+ visitors within its first three days after launch, demonstrating strong early interest in the platform.

    300+
    Website visitors

    First three days after launch.

    Figure measures website visitors, not registered or active users.

    Results so far

    Status
    In active developmentCore flows working end to end.
    Core modules
    6Resume, ATS, interviews, tracker, analytics, plan.
    Feedback loop
    Analyse → practise → applyOne connected system.
    Integrations
    MCP toolsResumes, jobs and reminders exposed to assistants.

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