Celabe Research Division
The research arm of Celabe. We work across the AI stack — real-time and conversational systems, LLM application architecture, multi-agent reliability, and human–AI trust — turning findings into systems and design that are fast, dependable, and keep people in calibrated control.
Latency, intent detection, and the systems engineering that make voice and chat AI fast and reliable in production.
Design patterns for LLM-integrated software — agent/tool protocols such as MCP, retrieval, and robust structured integration.
Using LLMs to build and optimise ML systems: model selection, hyperparameter tuning, and developer-in-the-loop workflows.
Hallucination, context drift, and synchronisation in multi-agent LLM systems — keeping distributed reasoning coherent.
Trust, reliance, over-reliance, and over-delegation — and the interface and governance design that keeps people in calibrated control.
LLM augmentation built to support professionals, not replace them — including clinical decision support designed for human oversight.
Author manuscripts shared as preprints. We hold ourselves to verifiable scholarship — every reference in our work is checked against canonical scholarly records before release.
C. Rodrigues, S. M. Rebello
A scoping review of 60 studies introducing the Personality–Trust Calibration (PTC) framework, extended to agentic LLMs.
C. Rodrigues et al.
A clinical decision-support recommender that sequences the next dental procedure from the whole patient chart — designed to augment the dentist, surface rationale, and abstain when unsure.
We welcome collaboration across applied AI — real-time systems, LLM architecture, multi-agent reliability, and human–AI trust and safety. For research enquiries, partnerships, or data sharing, get in touch.