Between tech innovation and human interaction: We train, fine-tune, and localise AI models
Authentic, structured communication data to help train, fine-tune, and localise AI models. The focus is not on scaling data, but ensuring it reflects the complexity, tone, and intent behind everyday human communication.
Programs
Supervised Fine-Tuning (SFT)
Involves training a base model using curated input-output pairs. The goal is to expose the model to high-quality, human-written examples across diverse use cases. We build SFT datasets that capture regional context, domain specificity, and varying levels of formality, enabling AI to respond more accurately in both professional and informal settings.
Reinforcement Learning with Human Feedback (RLHF)
Present a model with multiple plausible inputs and have your team grade the outputs. This feedback is then used to optimise the model’s reward function, helping it produce responses that align more closely with human expectations. Our contribution here includes building ranking frameworks, grading guidelines, and calibration protocols rooted in linguistic nuance and social awareness.
Vision Training
For multi-modal systems, collect and annotate image-caption pairs, scene descriptions, and visual references grounded in regional culture and everyday use. This improves a model’s ability to “understand” images in context, recognising objects, settings, or gestures that might be unfamiliar to generic datasets.
