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Category: AI Evaluation · Multimodal Data Annotation · Research Systems
Overview: Supported large-scale AI research initiatives by evaluating and annotating model outputs and multimodal data, helping translate complex machine-generated content into structured, high-quality training and evaluation datasets.
What We Did:
- Applied structured rubrics to evaluate AI-generated responses across multiple domains
- Assessed outputs for accuracy, clarity, coherence, and task alignment
- Generated and recorded natural-language descriptions of visual content for multimodal datasets
- Followed strict annotation, linguistic, and quality-control guidelines
- Collaborated with research and QA teams to maintain consistency across large-scale datasets
Outcome: High-quality annotated and evaluated datasets that improved model training, evaluation accuracy, and multimodal understanding across language, vision, and audio systems.
Nature's Symphony
Breathtaking colors of our planet
Faces of Humanity
Portraits of people from around the globe
Sands of Time
Stark beauty of desolate dunes
Beyond Boundaries
Visual odyssey across continents
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Internal R&D initiatives exploring AI-assisted storytelling, workflow systems, and creative infrastructure through SymbioSeed’s Concept-to-Code methodology.
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