Parse resume and job faster, more accurately
Parse resumes with Generative AI and LLM technology
A parsing solution that empowers you to streamline your recruitment process and make informed hiring decisions.
✅ Unparalleled Speed and Accuracy: We parse over 2 billion resumes and job postings annually with unparalleled speed and accuracy, ensuring you don’t miss a single qualified candidate.
✅ Multilingual Mastery: Extract and enrich data from extensive resumes in 29 languages and job postings in 9 languages, expanding your talent pool worldwide.
✅ Precision-Driven Candidate Matching: Swiftly filter, search, rank, and match candidates with pinpoint accuracy, eliminating time-consuming manual processes and enhancing your hiring efficiency.
Why choosing Textkernel’s parser?
As a seamless part of your ATS, CRM, or career portal, the Parser extracts data from resumes and job postings much faster than a seasoned recruiter and transfer structured data to your system at a fraction of the cost.
0.5 Seconds
parsing speed
2+ Billion
resumes and job postings parsed
1st Provider
of LLM-based parsing solutions
60% of HR Tech
trust our parsing solutions
LLM Parser
Unparalleled accuracy
Textkernel’s LLM Parser, fueled by Deep Learning, proprietary taxonomies, two decades of parsing experience, and ChatGPT’s natural language understanding, marks a significant leap forward in parsing technology, outperforming its competitors.
Document conversion: Using industry-standard solutions we convert input documents to text (for parsing) and HTML (for display). Combined with Textkernel’s Deep Learning engine that detects column layouts and document structure, we ensure that the LLM engine receives well-formatted text as input.
Parsing: parse key data points using GPT-3.5 using fine-tuned prompts
Normalization: normalize the extract data, enriched with mappings to taxonomies related to job titles, skills, education levels to standards like O*NET and ISCO
Inference: Our algorithms accurately compute additional metadata, such as the most recent use of a skill.
Output: Maintains the same JSON structure as our standard parser.
