Our Solution

Large Language Model (LLM) & Generative AI Tooling Solutions

In the realm of Generative AI development, managing data workflows presents common challenges, especially when interfacing with legacy systems and leveraging standard tooling. With our unique baseline tech stack, we offer solutions that range from refining existing tools to crafting fully customized, tailored solutions with a shared goal to streamline efficiency across the LLM development lifecycle.

Our solutions team collaborates closely with you to grasp your deep learning and LLM objectives while evaluating your existing workflow. This enables us to provide expert recommendations tailored to your specific tooling and data requirements.

Improve efficiency and reduce processing time

Multidimensional Quality Metrics (MQM) Tool

Our clients often deal with complex multilingual processes that require efficient and scalable solutions. They may need to manage thousands of files across multiple languages, requiring precise annotation.

We offer a customized quality metrics tool designed to perform comprehensive assessments on both source and target texts. It allows for detailed annotation according to the client's specific categories, subcategories, and style guides.

Our tool significantly improves efficiency, drastically reducing processing time per file and enabling a notable increase in the number of files managed per day. It's flexible design allows for the creation of additional versions to meet evolving needs.

Enhanced annotation solutions

Multiple Context Window Annotation

We offer enhanced annotation solutions catered to the needs of our partners. Our service is designed to streamline the multiterm annotation process for product titles within a vast ontology. Unlike traditional methods that rely solely on product page reviews, our solution provides contextual clarity for each item in the dataset.

Our system supports multiple context windows, including product page and product category displays, significantly improving overall accuracy and output. An additional feature allows annotators to identify category terms used across products. We also incorporate quality assurance modules to detect and rectify labelling inconsistencies through a comprehensive review process.