Extract candidate profiles from any resume format

Affinda’s core parser handles PDF, DOCX, ODT, and image files, outputting a structured JSON with sections for personal info, work history, education, skills, and certifications. It normalises dates, titles, and locations so downstream ATS logic stays consistent. For teams comparing AI data extraction tools, this pre-trained recruitment model often outperforms generic OCR.

Business outcome: Reduces the manual effort of candidate data entry by automating profile creation from any document, accelerating time-to-hire.

Standardise job requirements into structured fields

Feed a raw job description and the API returns a clean taxonomy of skills, experience levels, qualifications, and responsibilities. This structured output can feed matching algorithms or power a job taxonomy within your platform, ensuring every role adheres to the same data model.

Business outcome: Enables consistent candidate-job matching by eliminating the ambiguity of free-text job ads.

Score applicants against open roles using your criteria

The matching module compares a parsed resume against one or more parsed job descriptions and returns a relevance score plus component-level breakdowns. While the base score is useful, the real power comes when teams layer their own ranking logic—experience weighting, hard-skills filtering—on top of Affinda’s structured data.

Business outcome: Speeds up candidate shortlisting and reduces bias by moving from manual screening to data-driven scoring.

Automate accounts payable for recruitment agencies

Affinda’s invoice parser reads supplier invoices—even image-based ones—and returns line items, totals, dates, and vendor details. This is particularly valuable for recruitment firms handling a high volume of third-party staffing supplier bills.

Business outcome: Cuts down manual invoice data entry and reduces payment processing delays.

Parse resumes in the languages your candidates use

The platform supports parsing in dozens of languages without requiring separate models or configurations. This enables global staffing platforms to serve candidates in their native language, expanding addressable markets without multiplying development work.

Business outcome: Supports international hiring at scale without building per-language parsing pipelines.

Clean and normalise inconsistent document data

Affinda applies built-in validation rules to standardise formats—phone numbers, email addresses, date layouts—before returning the structured output. This reduces the need for additional cleanup scripts in your integration layer.

Business outcome: Improves data quality in your ATS or CRM, leading to better analytics and matching precision.

• Recruitment-specific data modelThe schema is purpose-built for hiring, meaning fields like skills and experience are already tuned to recruitment needs, unlike generic text extraction tools.
• High parsing accuracy across diverse formatsHandles PDFs, images, and DOCX files with reliable field extraction, even when resumes use complex layouts or tables.
• Clean developer experienceWell-documented REST API, client libraries in multiple languages, and a sandbox environment make integration straightforward for engineering teams.
• Cost alignment with usageThe credit model means startups can start small and scale costs with volume, rather than committing to a large upfront license.

• Per-document pricing adds up at scaleHigh-volume staffing platforms processing tens of thousands of documents monthly will see significant operational expenses unless volume discounts are negotiated.
• Matching quality depends on your ranking layerAffinda provides a baseline matching score, but achieving high-precision candidate-job alignment requires building custom ranking rules on top of its structured data.
• No out-of-the-box recruiter UIThe product is an API; non-technical teams cannot simply log in and parse resumes—they must integrate it into an existing system or build a front-end.
• Limited invoice template out-of-the-box coverageInvoice extraction works well for standard formats, but unusual or highly customised supplier invoices may need additional training or manual review.

Launch a new ATS with built-in parsing

A startup building an applicant tracking system uses Affinda’s resume and job description APIs to offer automatic profile creation from the first release. This dramatically reduces the go-to-market time while providing immediate value to early customers.

Automate candidate shortlisting for a staffing agency

A mid-size staffing firm integrates Affinda’s matching endpoint into its internal CRM. Recruiters upload job orders and instantly receive a scored list of candidates, cutting initial screening time by over half.

Streamline finance operations at a recruitment group

The finance team of a large recruitment company uses Affinda’s invoice extraction API to process hundreds of weekly supplier invoices, reducing manual data entry from hours to minutes and accelerating payments.

Build a multilingual talent marketplace

A global job matching platform taps Affinda’s multi-language parsing to accept resumes in any language, normalising them into a unified schema that supports its search and recommendation engines across 30+ countries.

Create a free Affinda account and generate your API key from the developer dashboard.

Choose a client library (Python, Node.js, Java, etc.) or start with the REST API to upload a sample resume and inspect the JSON response.

Map Affinda’s output fields (skills, experience, education) to your internal candidate schema and build a small integration script.

Run a batch of 50 real-world resumes through the parser and tune your matching criteria before moving to production.