How do you make it practical for organizations to train or fine-tune models on their own data, without requiring a machine learning team and a datacenter?
We research training methods that reduce the compute, data, and expertise required to produce useful domain-specific models. This includes work on parameter-efficient fine-tuning techniques (LoRA, QLoRA, and beyond), curriculum design for domain adaptation, and training stability improvements.
This research feeds directly into ScaiMind, our training orchestration platform, and shapes the fine-tuning workflows available to ScaiGrid users.
Running AI models is expensive — in compute, in energy, and in latency. We research techniques to make models more efficient without meaningful quality loss.
Our work covers quantization methods, model distillation, architecture optimization, and inference acceleration. We also explore fundamentally different approaches to neural computation, including binary and low-bit neural networks and alternative architectures that challenge the assumption that bigger always means better.
This research drives improvements across ScaiInfer (faster inference) and ScaiGrid (smarter routing based on cost-performance tradeoffs).
RAG is the bridge between AI models and real-world knowledge. But current RAG implementations leave a lot on the table: chunking strategies are crude, relevance ranking is imprecise, and multi-hop reasoning over retrieved content is fragile.
We research advanced retrieval architectures, embedding strategies, re-ranking methods, and techniques for combining structured and unstructured knowledge. The goal is RAG that reliably finds the right information and presents it to the model in a way that produces accurate, grounded answers.
This work flows directly into ScaiMatrix, our vector store module, and improves the quality of every RAG-powered interaction across the platform — from ScaiBot customer support to ScaiWave AI participants.
The transformer architecture dominates current AI, but it isn't the final word. We explore alternative and hybrid architectures, including attention mechanisms applied to non-transformer systems, sparse computation models, and approaches that trade raw scale for structural intelligence.
This is our most forward-looking research area. Not everything here will reach production next quarter — but the insights we gain inform our platform architecture and keep us prepared for the next shift in AI technology.
