One CV, fifty applications: it's the default strategy, and it's a losing one. Recruiters read for fit against a specific job description; a generic CV reads as generic. Tailoring by hand works, but an hour per application adds up fast when ...
One CV, fifty applications: it's the default strategy, and it's a losing one. Recruiters read for fit against a specific job description; a generic CV reads as generic. Tailoring by hand works, but an hour per application adds up fast when you're applying to ten jobs a week.
CVBoost is AI-powered CV tailoring built for Cameroon's job market. Upload your CV, paste the job description, get a tailored CV and cover letter back in seconds. No prompt engineering, no settings to configure.
Why Groq's LLaMA 3.1 70B
The model runs on Groq, and inference speed is the product decision here: the user is waiting mid-application, not kicking off a batch job. The pipeline takes two inputs — the candidate's existing CV and the target job description — and returns a CV reshaped for that role plus a matching cover letter.
Language and payments, handled locally
The interface works in French and English, both directions — a job market that doesn't run on English alone. Premium use is paid through CamPay, supporting MTN MoMo and Orange Money rather than assuming everyone has a card. There's also job scraper support, configured through environment keys.
Under the hood
Auth: JWT with httpOnly cookies
Performance: response caching plus rate limiting on Redis/Upstash
Stack: Node/Express, MongoDB (Mongoose), React 19, Vite, Tailwind v4
Takeaway
Tailoring is the step people skip when they're tired — which is exactly when they're applying. Automating that one step is the whole point.
Try it live: https://cvbuilder-obfc.onrender.com
Feedback welcome from job seekers and developers alike.