Applied AI Research
Our research focuses on practical, systems-level AI challenges. We build research systems that address real-world constraints—starting with MiniEmbed, an ongoing investigation into efficient embedding models.
Efficient Embeddings — MiniEmbed
Our current research focus. Exploring how to design embedding systems that are compact, efficient, and suitable for edge and latency-sensitive deployments while retaining strong semantic performance.
Model Compression & Quantization
Techniques to reduce model size and inference cost. Investigating quantization, distillation, and architecture-level efficiency improvements.
Information Retrieval Systems
Building efficient retrieval systems that integrate with smaller models. Focus on fast similarity search and practical integration patterns.
Systems-Level AI Research
Treating research and engineering as inseparable. Building systems that solve real problems under practical constraints.