Research and Development
We implemented the principles of Responsible AI in our research and development. A full-screen version is shown here.
Most of our customers come from regulated industries (the example above shows our approach to AI development in healthcare)
Examples of our developments are shown below.
Most of our customers come from regulated industries (the example above shows our approach to AI development in healthcare)
Examples of our developments are shown below.
Design Research
Deployment Research
Assurance Research
- Framework For Change - Socio-Technical Framework for AI development
- Atlas personalised AI learning and development for the: creative ; transport ; agriculture and construction sector
- CALMS a method of mitigating Bias and Risk of Harm in Large Language Models
- PLIM Prompt Language Model Improvement Method
- DioSim a cost effective way of modelling two-party dialogue agents (Therapy Case Study)
- Bev: a COM-B Avatar Agent
- DTxAI a method of reducing the risk in Digital Therapeutics AI
- AutoPrevis - a method to automate pre-visualisations using GENAI tools
- ComputeNovelty - users driven novelty, safety design in open ended systems
- Public Interest AI
Deployment Research
- Midas a selection of AI Agents designed to improve the investment readiness of digital health firms
- HAL trained to guide users about potential hallucination in LLM dialogue
- PriorArt a method to manage Prior Art and Copyright infringements
- UXAI NHS AI Agent Design and Testing
- ASRI - Using Chatbots in Adult ADHD Self Reporting Information Gathering
- PromptMH - a community of practice dedicated to making GenAI safe use in mental health
- Arun - increases the reach of VR based well-being support
- AutoESG trained to automate ESG evidence gathering and reporting
- Eli trained to elicit discussion about AI Risks in line with the IDEA Protocol (PoC in 2024)
Assurance Research
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