FixerCV: Building AI That Helps People Get Hired

The hiring process is broken in a specific, fixable way. Qualified candidates apply for jobs they're genuinely right for — and automated systems reject them before a human ever sees their resume. ATS (Applicant Tracking System) software scores and filters applications based on keyword matching, formatting patterns, and structural signals. Most candidates have no idea how that scoring works or what they need to do differently.

This is the problem FixerCV was built to solve.

What ATS Systems Actually Do

When you apply to a company that uses an ATS — which is most organizations with more than a few dozen employees — your resume doesn't go directly to a recruiter. It goes into a system that parses the document, extracts structured data, and scores the application against the job requirements.

These systems look for specific signals: keywords from the job description, properly formatted work history, clear section headers, quantified achievements, and relevant skills in the right context. A well-designed resume that uses graphics, columns, or tables may parse poorly and score low — not because the candidate is underqualified, but because the document format doesn't cooperate with the software reading it.

Most candidates don't know this. They spend hours designing for how a resume looks to a human reader, without optimizing for how it reads to a machine doing initial filtering.

What FixerCV Does

FixerCV analyzes a candidate's resume against the specific job they're applying for and identifies exactly what's working, what isn't, and what needs to change. The AI evaluates the same signals ATS systems use: keyword alignment with the job description, formatting compatibility, section structure, and how experience is described and quantified.

The output is specific and actionable — not "add more keywords" but exactly which terms to incorporate, where to place them, and how to reframe existing experience to align with what the job description signals the employer is actually prioritizing.

The Logic Behind It

The same logic that drives my work in global trade drives FixerCV: information asymmetry creates expensive outcomes for the less-informed party. Importers overpay because they can't see their real landed cost before buying. Job seekers get screened out because they don't know how the screening system evaluates them.

In both cases, the fix isn't complicated — it's access to information that was previously opaque. AI makes it possible to provide that information in real time, personalized to each person's specific situation.

Who It's Built For

FixerCV is for professionals actively looking for new roles — particularly those targeting competitive positions at companies that receive high application volume and use ATS software heavily. This includes most large employers, technology companies, financial institutions, and corporate organizations globally.

It's especially useful for candidates with strong underlying qualifications whose resumes aren't communicating that strength effectively to the systems doing initial screening.

The Bigger Picture

Hiring is one of the highest-stakes processes in people's professional lives, and outcomes are far too dependent on factors unrelated to actual job performance. FixerCV is a step toward correcting that imbalance — giving candidates the same visibility into how they're being evaluated that employers already have built into their systems.