AIQ is Nebuli’s suite of specialist and fully cited large language models (LLMs) that focus specifically on vertical knowledge built on our Deep Vertical Understanding (DeepVU) framework.

With the DeepVU framework, we trained AIQ’s models using Deep Reinforcement Learning from Human Feedback (DRLHF) methodology but with a focus on specific vertical and societal parameters that are relevant to specific business needs.

These parameters may include industrial operations, players, acronyms, issues and trends of a given domain while simultaneously addressing cultural, demographic and psychographic influences that may dictate outcomes and behaviours within this domain.

We also apply Federated Learning (FL), a distributed machine learning technique that allows multiple clients with their data and computation resources to collaborate to train AIQ’s models. This is particularly useful in cases involving private or sensitive datasets.

Using our Robotic Coworker™ framework, teams can automatically find the most suitable prompts and parameters to train their language models and third-party models without leaking out sensitive data.

We round extensive AI and LLM workshops to help your team enhance and optimise your natural language understanding (NLU) models supported by AIQ scoring mechanisms to improve performance and save costs. We help teams prevent common accidental data leaks from your organisation into third-party generative AI services, such as ChatGPT.

Furthermore, we help teams improve their prompts through A/B testing and randomised experimentation models to generate more accurate and relevant outputs to a given task.

With AIQ, we help organisations explore mitigation strategies against challenges that impede their successful development and deployment of responsible (“ethical”) AI and machine learning algorithms.