Case Studies

A behavioral training dataset was developed for a leading autonomous driving system company by classifying 100,000 short driving clips based on specific vehicle actions in 4 weeks with 95% accuracy. Each 4-second clip was reviewed by licensed annotators to identify the primary event, enabling training data to be prepared for behavior prediction and reinforcement learning modules.

A leading AI Lab partnered with our team to accelerate the training of an AI system designed to convert natural language prompts into fully functional React Next.js web applications of various complexities. It was accompanied by detailed chain-of-thought documentation that outlined how each prompt was interpreted and implemented. By combining synthetic data generation with expert human feedback and annotation, the project aimed to create comprehensive training examples that enhanced the AI’s ability to produce production-grade outputs with greater reliability, at unprecedented speeds: 1-2 web applications per day per person.

A global technology company engaged our team to conduct high-accuracy evaluations of AI-generated code responses across 25 programming languages. With the goal of improving large language model (LLM) performance through Reinforcement Learning from Human Feedback (RLHF), the project required consistent, expert-level assessments of code outputs under strict time constraints.

About 300 evaluation tasks were completed in just three weeks, each involving hands-on code testing, comparative analysis, and structured feedback using annotation protocols designed for scalable LLM training.