Using a human-centered, design thinking approach, we meet clients at their stage of AI maturity, successful adoption of AI and ability to scale
During the Discovery phase, we assess the client’s current state and level of AI-maturity. This collaborative deep dive into the business helps us to understand the nature of decision making and workflows, any barriers to AI adoption, and the nature of data collected – including how the data is acquired, performing exploratory data analysis, and looking at governance around the data. Deliverables are typically clear requirements and articulated value proposition.
During the Design phase, Using design thinking principals, we design a proof of concept, focusing on statistical validation of the model and the feasibility to develop and scale in the organization’s ecosystem. This includes iterative experimentation and learning, researching appropriate AI / ML methodology (model selection, feature engineering, testing and selecting different models), allowing us to further refine the problem statement. Deliverables are typically validated assumptions and a development plan.