If you’re building or managing an applicant tracking system (ATS), at some point you’ll face the question: should we build our own resume parser or use an off-the-shelf one?
It sounds simple, but the answer can shape your entire product roadmap. Resume parsing may be just one component of your platform, yet it’s also one of the hardest to get right – requiring complex AI, continuous optimization and airtight data security.
Unlike approaches based on large language models (LLMs), our trained machine-learning models deliver consistent, deterministic output – no hallucinations, no model drift and no dependency on external model calls. In short, our parser keeps everything predictable and under your control – keeping your customers’ candidate data safely contained and secure.
This guide explains what a parser does, what it takes to build one and why most ATS teams ultimately choose battle-tested, ready-made solutions.
What a resume parser does (and why ATS teams need one)
A resume parser is the engine that turns raw CVs into structured, searchable data – automatically extracting information like names, skills, job titles and education from any file format.
Instead of expecting your users to manually key in candidate details, integrating a parser gives your platform the ability to deliver cleaner profiles, faster workflows and more reliable downstream automation.
In most ATS setups, the parser sits quietly behind the scenes. When a candidate uploads a resume, an API call sends the file to the parser, which analyzes it using natural language processing and trained ML models. Within seconds, it sends structured data back to the database – filling candidate profiles, matching skills to job descriptions or powering autofill and enrichment workflows across your product.
For recruitment platforms, resume parser benefits are huge. High-quality parsing isn’t a “nice to have” – it’s now a baseline expectation from customers who want speed, accuracy and modern user experience (UX). With this in mind, many teams start brainstorming ideas to build a resume parser in-house before realizing the effort it demands.
How to build a resume parser (and what it really takes)
Building a resume parser from scratch sounds simple – until you realize what’s actually involved. A production-ready parser depends on a multi-layered tech stack that can read, understand and structure data from any CV. Here’s what it really takes:
- Set up your core pipeline: combine optical character recognition (OCR) to extract text from PDFs and images, natural language processing (NLP) to interpret meaning and machine learning (ML) models to classify and tag key entities like skills, education and work history
- Outline your taxonomies: define and maintain canonical taxonomies for skills, job titles, industries, degrees and certifications – including synonym mapping, hierarchical relationships and localization – so variants resolve to consistent concepts for matching and reporting
- Collect and label training data: you’ll need thousands of diverse resumes, accurately annotated to teach your models what to look for. This step alone can take months and requires ongoing updates to maintain accuracy as resume formats evolve
- Apply standardization scheme: apply a standardization layer and canonical schema (ISO dates, normalized job titles and company names, location codes, validated phone and address formats) so parsed fields are consistent and production-ready for search, deduplication and integrations
- Build entity extraction and validation logic: design rules or model layers that identify relevant fields, filter duplicates and map extracted data into a consistent schema your ATS can use
- Run continuous QA and model tuning: real resumes are messy – different layouts, fonts, languages and file types. To achieve high accuracy, you’ll need constant testing, retraining and error correction based on user feedback
Many ATS teams start with open-source libraries or Python-based frameworks to test the idea, but soon hit limits in accuracy and scalability. Even well-resourced teams underestimate how long it takes to reach enterprise-grade performance – and how quickly LLM-based approaches develop issues, like hallucinations, unstable field values and weaker security controls due to external model calls. This leads to the natural next step: evaluating whether to build or buy your parser.
Build vs buy: costs, timelines and choosing the right solution
Building your own resume parser gives you full control but comes with significant overheads:
- High development costs – data collection, model training, quality assurance (QA) and infrastructure all add up fast
- Ongoing maintenance – resumes constantly change format, so models need continuous updates to stay accurate
- Longer timelines – expect months (or more) before reaching production-level accuracy and reliability
- Security and compliance burden – handling sensitive candidate data means investing heavily in encryption, privacy and hosting safeguards
Buying or white-labelling a ready-made parser drastically shortens the time-to-value path:
- Faster go-to-market – integrate via API and ship within days, not quarters
- Predictable pricing – usage-based or annual credit models scale with your volume
- Continuous upgrades – your vendor absorbs the cost of R&D, accuracy tuning and compliance updates
- Reduced technical debt – your engineering team focuses on improving the ATS, not maintaining parsing pipelines
Case studies: ATS teams that scaled faster with Affinda
Here are two real-world stories of ATS teams that used our off-the-shelf AI resume parser to launch faster, scale with confidence, and skip the ongoing hassle of maintaining their own resume matcher – proof that ‘ready-made’ doesn’t mean compromise.
Reliable API integration and developer support
A recruitment software provider integrated our resume parser through its REST API to streamline candidate data ingestion. Their developers reported a smooth setup and immediate performance gains:
“This API-based integration returned reliable results for every format we sent to them. The communication from their team was above and beyond traditional customer experiences.”
With our developer-first documentation and responsive technical support, the team completed integration within weeks – not months – and achieved consistent, accurate parsing across every resume type tested.
Accuracy and multilingual coverage at scale
A global job board needed to process resumes from applicants in multiple languages without sacrificing accuracy. By leveraging our support for 50+ languages – including the ones critical to their market, such as English, French and Spanish – they achieved near-perfect extraction quality across their entire applicant pool.
Our trained ML models provided ATS-ready data for every submission – even complex or creative resume layouts. This allowed the team to automate resume parsing globally, reduce manual data entry and accelerate candidate matching across regions.
How to choose the right resume parser for your ATS
If you’re sold on the idea of an off-the-shelf solution, when evaluating vendors, look for:
- Accuracy and adaptability – consistent parsing across diverse formats, languages and layouts
- Ease of integration – REST APIs, client libraries and webhook support make implementation smoother
- Scalability – performance at both low and high volumes without latency issues
- Security and compliance – encryption in transit and at rest, ISO, SOC accreditation and GDPR alignment
- Optional modules – leading providers now include features like a resume redactor for anonymized hiring
- Transparent pricing – models that accommodate fluctuating resume volumes without penalty
- Proven results – seek customer references or test results to validate claims
Affinda meets all of these criteria. Our resume parser uses trained ML models (not LLMs) and is trusted by ATS vendors worldwide – delivering 99%+ accuracy to key fields, 50+ supported languages and a developer-friendly API that makes integration fast and dependable.
The bottom line: build smarter, not slower
For most ATS teams, learning how to make a resume parser isn’t the highest-leverage use of engineering time – not when proven solutions can deliver the same, if not better, results in days, not multiple sprints.
If you value quick time-to-value, accuracy and reliability, our resume parser makes it simple to integrate world-class parsing and start seeing ROI faster – so you can focus on building the parts of your ATS that truly set you apart.
