AI Research at Data Stella
We invest in applied AI research to build tomorrow's enterprise solutions today — publishing findings, open-sourcing tooling, and transferring breakthroughs directly into client engagements.
Research Areas
Six active tracks covering the full stack of modern AI — from foundation models to deployment infrastructure and responsible use.
Generative AI
Large language model fine-tuning, prompt engineering, and domain-adapted generation for enterprise use cases.
Natural Language Processing
Semantic understanding, named entity recognition, intent classification, and multilingual NLP for APAC markets.
Computer Vision
Object detection, document digitization, OCR pipelines, and visual quality inspection for manufacturing and retail.
Agentic AI Systems
Multi-agent orchestration, autonomous workflow execution, and tool-use frameworks for complex business processes.
RAG & Knowledge Systems
Retrieval-augmented generation, vector databases, and hybrid search to ground AI on proprietary enterprise data.
Responsible AI
Bias detection, model explainability, data privacy frameworks, and AI governance aligned with Singapore AI guidelines.
Our Research Methodology
Applied research built to ship — every finding is validated against real enterprise workloads and deployed into production.
Identify
Surface high-impact problems from client engagements and industry signals.
Prototype
Rapid experimentation with state-of-the-art models, techniques, and architectures.
Validate
Rigorous benchmarking against baselines and real-world enterprise datasets.
Deploy
Transfer proven techniques directly into client solutions and our product suite.
Insights & Publications
Technical deep-dives, white papers, and research briefs from our AI lab — grounded in real deployments.
Deploying RAG in Enterprise Environments: A Singapore Case Study
How we reduced hallucination rates by 94% using hybrid retrieval and re-ranking across a 2M-document knowledge base.
Fine-tuning LLMs for Domain-Specific Customer Support in APAC
Benchmark results across five vertical-specific fine-tuned models vs. general-purpose LLMs on Bahasa, Thai, and Mandarin support tickets.
Agentic AI: Building Autonomous Business Workflows at Scale
Architecture patterns, failure modes, and production lessons from deploying multi-agent systems across logistics and finance clients.
Partner with Our AI Research Lab
We collaborate with enterprises, universities, and government agencies on applied AI research. Get early access to findings, co-develop models on your data, and accelerate your AI roadmap.