A comprehensive rubric-based evaluation framework for assessing LLM outputs across accuracy, reasoning, coherence, and safety dimensions. Used to evaluate 500+ model responses with consistent scoring.
View on GitHub →Automated data analysis pipeline using Model Context Protocol for structured data extraction, transformation, and insight generation. Processes 10K+ records per run.
View on GitHub →Market intelligence platform tracking 50+ AI SaaS products with automated competitor analysis, pricing monitoring, and sentiment aggregation from 500+ review sources.
View on GitHub →Designed and implemented multi-agent orchestration workflows for automated task delegation, quality control, and structured output generation across diverse analytical contexts.
View on GitHub →Response evaluation, rubric design, prompt engineering, red teaming, bias detection, factuality assessment, inter-rater reliability
Python (pandas, numpy, scipy), SQL, statistical analysis, data visualisation, A/B testing, trend analysis, quantitative research
Structured literature review, qualitative analysis, market intelligence, competitive analysis, user research synthesis, technical writing
Git, GitHub Actions, SQL databases, Google Analytics, Looker Studio, API integration, CI/CD pipelines, Linux, cloud platforms
Email: mohamed.origami@gmail.com · Cardiff, UK
Available for AI evaluation, data analysis, and research consulting projects