About Triple R

I built Triple R after finding myself on the other side of the hiring process.

After being made redundant, I was suddenly back in the job market. I thought I had a pretty good CV, but like most candidates, I had no idea how it was actually landing with recruiters. So I decided to do something simple.

I asked a number of recruiters to roast it.

Not politely review it. Properly roast it.

Some of them gave genuinely helpful feedback. Others said things like "your CV is fine," which I knew wasn't true. If it was fine, I wouldn't have been struggling to convert applications into interviews.

Around the same time I started seeing the exact same thing happening to other people on LinkedIn. Candidates talking about applying to hundreds of jobs and hearing nothing back.

No feedback. No signal. No idea what they were doing wrong.

It's the age-old application black hole.

I've worked in recruitment and job board platforms for about 15 years, most recently as a VP of Product, so I've seen this problem thousands of times. In that time I've worked on the systems that match candidates to jobs and seen first-hand how those decisions actually get made.

Candidates are trying to improve, but the system gives them almost no useful information.

Most career sites try to help by giving you a match score. You upload your CV, compare it to a job description, and you get something like a 3 out of 5 rating.

But that's usually where the help stops.

You get a score, but no real explanation. No context. No clear idea what you should actually change.

And that's the frustrating part. A score doesn't help you improve.

Triple R flips that model on its head.

Instead of just giving you a score, it shows you the reasoning behind it. The gaps. The strengths. The things a recruiter would actually notice when they review your CV.

I originally built Triple R inside GPT just to see if it would help people. Thousands of people started using it and the early version reached a 4.6-star rating. The message from users was clear: people didn't want polite, generic responses. They wanted honest feedback that would actually help them improve.

So I decided to build Triple R properly.

Today, Triple R evaluates your CV against a specific role the way a recruiter would. It looks at your experience, the job requirements, and the expectations for the role, then gives you a realistic assessment of your chances.

You paste in your CV and a job description (or a link to one), and Triple R generates a structured analysis covering:

• •how well you match the role.
• •the strengths and weaknesses of your CV.
• •skills or experience you may be missing.
• •potential red flags a recruiter might notice.
• •and a realistic assessment of your chances.

From there it can help you go further. Triple R can:

• •act as a recruiter relevant to your industry and location
• •explain your match rating and why you did or didn't score highly
• •point out missing skills or experience
• •tell you honestly if you're not a match and suggest what you might look for instead
• •help you write a cover letter in your own voice
• •help optimise your LinkedIn profile, including suggested posts to build visibility
• •give you interview preparation tips
• •prompt you to think about whether the role is actually a good cultural fit
• •rewrite suggestions in the tone of your CV rather than copying from the job description

Triple R is designed to be direct.

Most candidates only ever receive polite feedback. Friends say their CV looks great. Generic online tools check formatting. Career advice online tends to be vague and encouraging.

But that doesn't tell you whether a recruiter would actually put you through to interview.

Triple R is designed to simulate the kind of internal recruiter assessment candidates almost never see.

That means the feedback can be blunt.

Not to discourage you, but to show you the gaps clearly so you can fix them. Because polite feedback doesn't get people hired. Honest feedback does.

Triple R is powered by Claude by Anthropic, one of the most advanced AI models available.

But this isn't generic AI output. The prompts and evaluation framework were designed based on how recruiters actually review CVs — drawing on 15 years of experience in recruitment and hiring platforms.

The goal is to mirror the way a real recruiter would evaluate your application, then make that reasoning visible to you in plain language.

Your CV isn't stored, reused, or used to train models.

Triple R uses Claude Sonnet by Anthropic — a model that understands the nuanced differences in job applications, not just keyword matching.

It can read between the lines of a CV the way a recruiter would — picking up on career progression, role fit, seniority signals, and gaps that a simple scoring tool would miss.

This isn't about replacing human recruiters. It's about giving you the same level of insight they have, so you can improve your application before they see it.

We're giving you the tools to create the most successful applications you can and save recruiters and hiring managers time in the process.

Anthropic does not use your data to train its models. You can review Anthropic's data retention and privacy policies at anthropic.com/privacy.

Triple R is built and operated by Feenra Ltd, a company registered in England and Wales (Company number 16351871).

And yes, it's just me.

After years working in recruitment platforms, I'd seen how opaque the hiring process is for candidates. Most people are trying hard to improve but simply don't get the information they need.

Triple R is my attempt to fix that in a practical way.