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<!doctype html>
<html lang="en">
    <head>
        <meta charset="UTF-8" />
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        <title>KR Labs</title>
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    </head>

    <body>
        <header>
            <h1>KR Labs</h1>
            <nav>
                <a href="#about">About Us</a>
                <a href="#products">Products</a>
                <a href="#contact">Contact</a>
            </nav>
        </header>

        <section class="content" id="about">
            <h2>About Us</h2>
            <p>
                KR Labs develops transparent and trustworthy AI solutions for
                natural language processing (NLP) applications. Founded by NLP
                researchers
                <a href="https://hlt.bme.hu/en/kovacsadam">Ádám Kovács</a> and
                <a href="https://informatics.tuwien.ac.at/people/gabor-recski"
                    >Gábor Recski</a
                >, with AI business leader
                <a href="https://www.linkedin.com/in/ashishanandroy/"
                    >Ashish Roy</a
                >, the company bridges academic innovation with enterprise
                needs. Our products are designed to empower AI application
                developers, organizations, and individual users. We build
                open-source technology that combines novel statistical and
                rule-based methods, originally developed at
                <a href="https://nlp.ec.tuwien.ac.at/">TUW-NLP</a> and
                <a href="https://hlt.bme.hu/en/">HLT-Budapest</a>. From 2023 to
                2024, KR Labs was incubated at the TU Wien Innovation Incubation
                Center (i²c).
            </p>
        </section>

        <!-- Products Section -->
        <section class="products" id="products">
            <h2>Our Products</h2>
            <div class="product-item">
                <h3>Lettuce<span style="color: #ff5757">Detect</span></h3>
                <p>
                    <b>LettuceDetect</b> is a hallucination detection framework
                    designed for Retrieval-Augmented Generation (RAG) systems.
                    Built on
                    <a
                        href="https://github.com/AnswerDotAI/ModernBERT"
                        target="_blank"
                        >ModernBERT</a
                    >, it identifies unsupported segments in LLM-generated
                    answers in real time. It overcomes the limitations of short
                    context windows and the high computational costs associated
                    with large LLM-based detectors.
                </p>
                <ul>
                    <li>
                        <strong>Token-Level Detection</strong>: Flags
                        unsupported parts of answers.
                    </li>
                    <li>
                        <strong>Long-Context Support</strong>: Processes up to
                        4K tokens.
                    </li>
                    <li>
                        <strong>High Throughput</strong>: 30–60 examples per
                        second on a single GPU.
                    </li>
                    <li>
                        <strong>Open Source</strong>: Released under the MIT
                        License.
                    </li>
                </ul>
                <p>
                    <a
                        href="https://github.com/KRLabsOrg/LettuceDetect"
                        target="_blank"
                        >Learn More on GitHub</a
                    >
                    •
                    <a
                        href="https://pypi.org/project/lettucedetect"
                        target="_blank"
                        >Download on PyPI</a
                    >
                    •
                    <a href="https://huggingface.co/KRLabsOrg" target="_blank"
                        >View Models on Hugging Face</a
                    >
                </p>
            </div>
            <div class="product-item">
                <h3>Verbatim<span style="color: #ff5757">RAG</span></h3>
                <p>
                    Traditional RAG systems retrieve relevant documents and then
                    allow an LLM to freely generate responses based on that
                    context. This can lead to hallucinations where the model
                    invents facts not present in the source material.
                    <b>VerbatimRAG</b> is our RAG framework that prevents
                    hallucination by ensuring all generated content is
                    explicitly derived from source documents.
                </p>
                <ul>
                    <li>
                        <strong>Minimalistic</strong>: We provide a minimalistc
                        end-to-end RAG system and many additional
                        functionalities that you can integrate into your own
                        solution.
                    </li>
                    <li>
                        <strong>How it works</strong>: We use extractive models
                        to mark the relevant parts of the context and then
                        implement dynamic template generation that is used to
                        generate the final response.
                    </li>
                    <li>
                        <strong>Open Source</strong>: Released under the MIT
                        License.
                    </li>
                </ul>
                <p>
                    <a
                        href="https://github.com/KRLabsOrg/Verbatim-RAG"
                        target="_blank"
                        >Learn More on GitHub</a
                    >
                </p>
            </div>
        </section>

        <section class="content" id="contact">
            <h2>Contact Us</h2>
            <p>
                <center>
                    Email: <a href="mailto:info@krlabs.eu">contact@krlabs.eu</a>
                </center>
            </p>
        </section>

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