Most ATS platforms promise automated CV parsing. Upload a resume, and the system extracts the data automatically. Sounds great in theory.

The reality? You spend 5-10 minutes per CV fixing mistakes:

• Names parsed as job titles

• Skills listed under experience

• Dates completely wrong

• Education mixed with work history

If you've ever wondered what CV parsing actually is, the concept is simple. The execution is where most tools fall apart.

This isn't just annoying — it defeats the entire purpose of automation. If you're manually correcting every candidate profile anyway, what exactly is the software doing for you?

The dirty secret of CV parsing accuracy

Vendors love to claim "95% accuracy" or "99% extraction rates." These numbers are technically true — and practically meaningless.

Here's why: accuracy is usually measured per field, not per CV. If a parser correctly extracts 19 out of 20 fields, that's 95% accuracy. But if that one wrong field is the candidate's current job title or years of experience, the entire profile is unreliable.

For recruiters, what matters is: how many CVs can I trust without checking?

With most parsing tools, the honest answer is: not many.

Why traditional CV parsing struggles

The template problem

Old-school parsers work by matching CV layouts to templates. They expect:

• Name at the top

• Contact info in a specific format

• Sections labelled "Experience" and "Education"

• Dates in a consistent format

But candidates don't follow templates. They use creative layouts, unusual section headers, tables, columns, graphics. Every variation breaks something.

The keyword trap

Many parsers rely heavily on keywords. They look for "Experience" to find work history, "Skills" to find competencies. But what about:

• "Professional Background" instead of "Experience"

• "Technical Proficiencies" instead of "Skills"

• "Academic Credentials" instead of "Education"

Keyword-based parsing misses these variations constantly.

The context blindness

Traditional parsing can't understand context. It sees text and tries to categorise it based on position and patterns. But the same text means different things in different contexts:

• "Python" under Skills = a programming language

• "Python" under Experience = might be part of a company name

• "5 years" could be tenure at one job or total experience

Without understanding context, parsers make confident wrong guesses.

What actually breaks during parsing

Based on thousands of CVs processed, here are the most common failures:

Names and contact details

You'd think this would be easy. It's not.

• Double-barrelled surnames get split incorrectly

• Middle names become last names

• Non-Western name orders confuse parsers

• Phone numbers with unusual formatting get mangled

• Email addresses inside images are missed entirely

Work experience

The most valuable section, and the most error-prone:

• Job titles and company names get swapped

• Date ranges are misinterpreted

• Multiple roles at one company merge together

• Freelance or consulting work disappears

• Gaps get filled with wrong data

Skills

Skills extraction is particularly unreliable:

• Skills mentioned in job descriptions get attributed to the candidate

• Skill levels ("basic", "advanced") get lost

• Related skills get merged or separated incorrectly

• Soft skills and hard skills aren't distinguished

Education

• Degree levels misidentified

• Institution names truncated or wrong

• Graduation dates confused with enrollment dates

• Certifications mixed with formal education

The real cost of bad parsing

Bad parsing doesn't just waste time on corrections. It has downstream effects that hurt your entire recruitment process.

Search becomes unreliable

If skills are extracted incorrectly, your candidate search returns wrong results. You miss qualified candidates and waste time on unqualified ones. This is why AI-powered search needs clean data to work properly.

Reporting is meaningless

Trying to track recruitment KPIs with bad data? Your metrics will be wrong. Average years of experience, skill distributions, source quality — all unreliable.

Candidate experience suffers

When you reach out to candidates based on incorrect profile data, it shows. "I see you have 10 years of Java experience" when they have 2 years damages your credibility.

You stop trusting your database

The worst outcome: recruiters stop using the database entirely. They go back to searching LinkedIn for every role because they can't trust their own candidate pipeline. All that effort building a talent pool — wasted.

What good CV parsing looks like

So what separates parsing that works from parsing that doesn't?

AI that understands context

Modern AI-powered CV parsing doesn't just pattern-match. It reads the CV like a human would, understanding that context determines meaning.

When AI sees "Led a team of 5 developers to deliver Python-based microservices," it understands:

• This is work experience

• Python is a skill being used

• Team leadership is demonstrated

• 5 is a team size, not years of experience

This contextual understanding dramatically reduces errors.

Handling diverse formats

Good parsing works regardless of:

• File format (PDF, Word, even images)

• Layout (single column, multi-column, creative designs)

• Language (especially important for multilingual recruitment)

• Section naming conventions

Confidence scoring

The best parsers don't just extract data — they tell you how confident they are. Low confidence on a field? Flag it for review. High confidence across the board? Trust it and move on.

This lets recruiters focus verification effort where it matters instead of checking everything.

Graceful degradation

When something can't be parsed reliably, good systems leave it blank rather than guessing wrong. An empty field is better than incorrect data that looks correct.

How to evaluate CV parsing quality

Before committing to any ATS or parsing tool, test it properly:

Use your actual CVs

Don't rely on demos with cherry-picked examples. Upload CVs from your real candidate pool — including the messy ones, the creative ones, the scanned ones.

Check the hard cases

• CVs with non-standard layouts

• Non-English CVs (if relevant to your market)

• Scanned documents or image-based PDFs

• CVs with tables and columns

• Very long CVs (5+ pages)

• Very short CVs (1 page, minimal info)

Verify the details

Don't just glance at the parsed output. Compare it field-by-field with the original. Check:

• Are dates exactly right?

• Are job titles accurate?

• Are skills correctly attributed?

• Is nothing important missing?

Calculate real accuracy

Count how many CVs you could use without any corrections. That's your real accuracy rate — the one that actually affects your time to hire.

The bottom line

CV parsing is supposed to save time. When it works, it's transformative — you can stop reading CVs manually and focus on actually recruiting.

When it doesn't work, it's worse than useless. You're paying for software and still doing data entry.

The difference comes down to technology. Rule-based parsing from the 2010s can't handle modern CV diversity. AI-powered parsing can.

If your current parsing forces you to correct every profile, you don't have a parsing problem. You have a technology problem. And it's solvable.

Adeptiq's AI-powered CV import is built on modern language models that understand context, not just patterns. Upload a CV and see the difference — structured candidate profiles you can actually trust. Try it free.