Parse new possibilities

Introducing Textkernel’s LLM Parser

Unveiling Textkernel's LLM Parser (beta)

The recruitment landscape is rapidly evolving, and modern companies require technology that can adapt to these changes to maintain a competitive edge in the hiring and talent management process. While resume parsing has long been a staple, Textkernel has been at the forefront of global-leading solutions in this area. Yet, the contemporary demand for more nuanced information about candidates has emerged as the new frontier in recruitment competitiveness.

Large Language Models (LLMs), like Open AI’s ChatGPT, offer a unique blend of human-like domain understanding and industrial scalability that allows parsing to be taken to a new level. This advancement allows for a wealth of information to be extracted from resumes, leading to a deeper understanding of candidates.

Unlock New Possibilities

Welcome to the future of resume parsing with Textkernel’s LLM Parser (beta), where we seamlessly fuse Textkernel’s robust, enriched proprietary parser with the capabilities of Open AI’s ChatGPT. This revolutionary fusion brings you a parsing solution like no other.

With Textkernel’s LLM Parser, we’ve taken the power of Textkernel’s Deep Learning expertise, proprietary taxonomies and two decades of parsing expertise, and married it with Open AI’s ChatGPT natural language understanding prowess. The result is a parsing solution that offers unparalleled accuracy, potential limitless flexibility in data extraction, and the ability to unlock a world of possibilities in talent acquisition.

Explore the future of parsing technology with Textkernel’s LLM Parser and experience the synergy of expertise, innovation, and unmatched performance. Get ready to revolutionize the way you parse resumes and embark on a journey towards better, more efficient recruitment. Your next-generation parsing solution awaits.

Trusted Expertise

We carefully crafted a parser that combines the strengths of GPT with our industry-leading parsing technology and skills and professions intelligence. The result is a better parser than can be made with either technology independently.

Ruud Liebregts

Chief Product Officer

Bringing together the best of both worlds

Textkernel’s LLM Parser (beta) represents the best of two exceptional worlds. On one hand, we offer our trusted, fast, dependable, and compliant talent data capture. On the other hand, we harness the global knowledge and precise natural language comprehension in LLM models like GPT.


(*) Textkernel’s LLM Parser is accessible via Sovren, a Textkernel company. Simply create a demo account on the Sovren platform to start using it in real-time.

We take care of the entire parsing pipeline, which entails more than just GPT:

  1. 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 GPT engine receives well-formatted text as input.

  2. Parsing

    Using fine-tuned prompts optimized for accuracy and speed, we parse key data points using GPT-3.5.

  3. Normalization

    The extracted data undergoes normalization through Textkernel’s modules, which ensures consistent output. The data is enriched with mappings to taxonomies related to job titles, skills, education levels. While we also map to standards like O*NET and ISCO, Textkernel’s real-world taxonomy of skills and job titles sets us apart.

  4. Inference

    Our algorithms accurately compute additional metadata, such as the most recent use of a skill or the total months of experience with a skill.

  5. Output

    The result from Textkernel's LLM Parser maintains the same JSON structure as our standard parser and seamlessly integrates with other Textkernel products.

Uncover a new world of Parsing possibilities

Textkernel’s LLM Parser (beta) paves the way for future game-changing parsing developments. Imagine having precise answers to specific questions when reviewing a candidate profile:

  ✓   Has this person ever worked in a Fortune 500 company?
✓   Is this candidate experienced in B2B environments?

  ✓   Has this applicant ever worked abroad?
  ✓   Does a candidate have all certifications needed for regulated jobs like nursing?

Textkernel’s LLM Parser (beta) is the foundation for future resume parsers that will be just as precise as they are flexible and customizable. Join us on this journey.


Experience the possibilities *

(*) Textkernel’s LLM Parser is accessible via Sovren, a Textkernel company. Simply create a demo account on the Sovren platform to start using it in real-time.


Why Textkernel and Textkernel's LLM Parser?

  1. Continuous Innovation

    At Textkernel, innovation fuels our growth and inspires us to continuously improve our solutions. We are the first established parsing provider to adopt GPT, demonstrating our deep-rooted commitment to innovation for a competitive edge and allowing you to focus on your business.

  2. Ease of Use

    Textkernel now offers multiple world-class parsing solutions, each meeting specific needs but all delivering superior results. Benefit from quick, self-provisioning setup from the leading parsing provider, delivering results optimized for speed and accuracy.

  3. Future-Readiness

    Benefit from future proof technology without complicated API setup, or prompt engineering and LLM selection investments. If you require specific and tailored information parsed from your CVs and applications, Textkernel's LLM Parser provides the foundation for answering these questions.

  4. ​Trust and Expertise

    Textkernel's LLM Parser is built upon the solid foundation of our specialized resume parsing technology. Over the past two decades, we've consistently been at the forefront of adopting AI advancements, including Deep Learning, and introducing them to the market responsibly and fully compliant. In this context, GPT represents a continuation of our ongoing commitment to innovation and excellence.

Next level CV Parsing Excellence with a trusted foundation

As the global leader Resume Parsing solutions, we’re trusted by 8 out of the top 10 global staffing agencies, parsing over 2 billion resumes each year. With the emergence of LLM technologies, we’re excited to offer our customers a fusion of the best of both worlds, marrying our world-class parsing technology with the incredible world knowledge and language processing of LLMs, delivering an unparalleled experience for our 2.500+ customers.

How to get access to Textkernel's LLM Parser?

If you’re a Textkernel customer, connect with us and let’s discuss how Textkernel’s LLM Parser can help you achieve your hiring goals!

Want to try it? Textkernel’s LLM Parser is delivered through Sovren, a Textkernel company. Create a demo account on the Sovren platform and you are ready to experience our latest parsing innovation.

Existing Sovren customers just need to log in with their customer details.

LLM Parser FAQs

Whether you're looking for insights on functionality, integration, or data security, you'll find valuable information to streamline your experience and make data-driven decisions with ease. If you have a question that's not covered here, don't hesitate to reach out for personalized assistance.

  • Textkernel’s LLM Parser demonstrates the advanced capabilities of our technology. You can assess whether it’s suitable for your use cases or if our standard parser remains the better choice. 

    Our standard parser is known for its speed and impressive accuracy, achieving over 95% accuracy for the most critical data points. Textkernel’s LLM Parser elevates accuracy even further, reducing the remaining errors by up to 30%. However, it’s important to note that it currently requires more time for parsing and comes at a higher cost.

  • If you’re already using the Sovren API, there’s no need for modifications. The LLM engine utilizes the same API models and endpoints as the existing Sovren API. You can enable Textkernel’s LLM Parser with a single boolean flag on the request. 

  • Yes, the CV/Resume is sent to a private OpenAI Azure model and is not stored. OpenAI does not have access to this data, and it is not used to improve their models.

  • Initially, our focus was on English to showcase the capabilities of LLM technology. However, we plan to expand the GPT engine to include more languages in the future.

  • No, when you enable the LLM Parser and we detect a non-English document, we will return an error and not charge the add-on transaction cost.

  • If you’re using the Sovren API, our LLM Parser will produce the same data model that Sovren AI Matching uses, making it compatible. 

    However, if you’re using the Textkernel API, you’ll need to integrate the Sovren API for Parsing. Please reach out to for tailored advice regarding your setup.

  • By combining Textkernel technology with Open AI’s ChatGPT, our solution is up to four times faster compared to using ChatGPT as a standalone parser. We manage the entire parsing process, including document conversion to text and HTML, as well as handling column layouts. Moreover, we enhance the output with our data-driven skills and profession taxonomies. By choosing Textkernel’s LLM Parser, you eliminate the need to spend any time selecting, evaluating and tuning the LLM technologies and ensures you benefit from future enhancements and features.

  • At this stage, Open AI’s ChatGPT is the most viable option. However, once you’ve integrated with our API, you will automatically benefit from any future changes or improvements in the underlying LLM technology.

  • We employ several strategies in our prompts to minimize the impact of hallucinations. In our evaluations of the parsing accuracy, hallucination issues have rarely been observed. Overall, Textkernel’s LLM Parser offers improved accuracy compared to the standard parser, helping to offset any potential hallucinations.

Ready to get started?

Choose your path to innovation

Seize the opportunity to transform your recruitment processes with Textkernel's LLM Parser (beta).Witness the future of recruitment technology in action.