ATS Data Extraction and Validation with Simply

Why your ATS data is a problem

Every recruitment agency has an ATS. And every recruitment agency has a data problem. Not because the ATS is bad, but because the data going into it rarely has the quality you need.

It starts with input. Recruiters enter data manually, under time pressure, with inconsistent formats. One colleague writes dates as "01/03/2024", another as "March 2024", a third as "2024-03-01". The same goes for phone numbers, addresses, job titles, and industry classifications. After a year, you have a database full of variants of the same information.

And then there's extraction. Getting CVs in is easy. Getting the right data out and placing it in the right spot in your ATS, that's where things go wrong.

The extraction problem

Data extraction from CVs sounds simple. Read the document, pull out name, contact details, work experience, and education, and put them in the right ATS fields. But in practice, dozens of things can go wrong.

The first problem is format variation. CVs come in PDF, Word, as images, as scans, sometimes even as LinkedIn profiles copied into an email. Each format requires a different extraction method. Traditional parsers struggle with that variation.

The second problem is structure variation. No two CVs are built the same way. Some candidates start with education, others with work experience. Some use tables, others free text. Some list skills with each job, others in a separate section. A parser that depends on fixed structures fails with the majority of CVs.

The third problem is language variation. Dutch CVs, English CVs, bilingual CVs, CVs with German jargon from the technical sector. The parser needs to not only recognize text, but understand what language and context that text is in.

Why extraction without validation is dangerous

Imagine: your parser extracts data from a CV and fills your ATS automatically. Sounds fantastic. But what if the parser interprets "2019-2023, ABN AMRO" as an education period instead of work experience? Or if the reference's phone number gets picked up as the candidate's phone number?

Without validation, you don't notice those errors. They disappear into your database and surface weeks later when you call a candidate on the wrong number. Or when your client receives a CV where work experience is listed as education.

Every extraction without validation is a gamble. And in recruitment, you can't afford to gamble. Your reputation is on the line with every candidate you present.

How Simply combines extraction and validation

Simply's approach is built on a core principle: no data without verification. The system performs three steps for every CV:

Step one: context-aware extraction. The AI parser reads the CV and understands the content. Not by searching for page positions, but by interpreting the meaning of the text. "Senior Consultant at Accenture, 2020-present" is recognized as work experience, with job title, employer, and period. Regardless of formatting, language, or position in the document.

Step two: intelligent mapping. The extracted data gets linked to the correct fields in your ATS. Not just text fields, but also dropdowns, enums, and formatted fields. The data extraction knows that "Bachelor's" in your education dropdown corresponds to "BSc" on the CV. And that "California" is the correct state for a candidate from San Francisco.

Step three: transparent validation. Every extracted field gets a confidence indicator. The transparency system shows green for certain values and orange for values that deserve a quick check. You can click on any field to see the source data from the CV.

The green and orange system in practice

Let's look at a concrete example. A candidate sends a CV in PDF format. Simply processes it and shows the following:

Name: John Smith. Green. Email: j.smith@email.com. Green. Phone: +1 555 0123. Green. Current role: Senior Project Manager. Green. Industry: Construction. Orange.

Why is "Industry" orange? Because the CV mentions the candidate works at a construction company, but also has experience in building services engineering. The system isn't sure which industry is primary. You look at it, select "Construction" and move on. Cost: five seconds.

Without this system, you'd have had two options: manually verify everything (costs minutes per CV) or blindly trust the parser (risk of errors). The green-orange system gives you the best of both worlds.

The connection to your ATS

Extraction and validation are only valuable if the data actually ends up in your ATS. Simply integrates with the systems you already use. Bullhorn, Salesforce, Carerix, Connexys, Mysolution.

The CRM data entry is bidirectional. The system knows your ATS structure: which fields exist, which are required, which formats are expected. New candidate data gets automatically placed in the right spot. And when your agency has custom fields (and every agency does), those are included.

No CSV exports. No manual imports. No switching between systems. Data goes from the CV to your ATS in a straight line, with validation along the way.

What this means for your daily work

A recruiter at an agency processing twenty CVs per day spends two to three hours on data entry and verification without automation. With Simply's extraction and validation system, that drops to twenty to thirty minutes. The data isn't just entered faster, it's also cleaner.

Those two hours you save? You spend them on work that matters. Calling candidates. Meeting clients. Winning vacancies. That's recruitment. Data entry is not.

And the cumulative effect is even bigger. After three months, you have an ATS database that's significantly cleaner than before. Searching works better. Insights and reports are more reliable. And your team spends structurally less time on admin.

Security and compliance

Candidate data is personal data. You handle it with care. Simply is ISO 27001 certified and fully GDPR compliant. All data is processed according to the strictest security standards.

That means: data is stored and transmitted encrypted. There's a complete audit trail of who viewed or edited which data and when. Candidate data is not used for training AI models. And when a candidate submits a deletion request, you can trace exactly where their data sits.

For agencies working with sensitive candidate data (think finance, legal, government), this isn't optional. It's a requirement. And Simply meets it.

From standalone tools to a system

Most agencies use a combination of separate tools. A parser here, a formatting tool there, manual data entry in between. The result: inefficiency, inconsistency, and frustration.

Simply combines extraction, validation, formatting, and data entry into one system. Not as an all-in-one tool that does everything mediocrely, but as an integrated platform where each component strengthens what the others do.

The parser delivers data to the validator. The validator sends clean data to your ATS. The formatting engine uses the same parsed data to generate a professional CV. Everything works together, everything is consistent, everything is traceable.

Curious how this fits your workflow? Try Simply for free or read how eliminating manual CV processing works in practice.

Extraction at high volumes: scalable without compromise

As your agency grows, data volume increases exponentially. More conversations, more candidates, more CRM updates. Manual entry doesn't scale. With every new recruiter, the chances of input errors, inconsistent formatting, and missed fields increase. Automatic extraction with validation solves this without requiring additional control steps.

Simply processes every recording the same way, whether you conduct ten or two hundred conversations per week. Extraction rules remain consistent. The validation system with green and orange markers works identically for every recruiter on the team. This gives operational managers confidence in data quality, even when the team is growing rapidly or temporary staff are brought in.

An additional benefit is the system's learning curve. The more conversations Simply processes, the better the extraction aligns with your specific ATS fields and terminology. After a few weeks, the system recognizes that your agency uses 'availability date' where others say 'start date.' This fine-tuning happens automatically and reduces the number of orange validation flags over time.