We don’t do AI for AI’s sake. Instead, we work with you to enable AI within the context of a broader solution designed to effect particular business outcomes. Our experience in solution deployment can also help with understanding what you need to do to prepare for launch.
Our engagement methodology is summarized below.
We inspire ideation from your team by first defining artificial intelligence, machine learning, and data science, and providing some examples. Since AI solutions involve much more than just AI, we also give examples of implications to business operations and what's potentially involved when launching your AI solution.
We facilitate a use case workshop (or support your enterprise idea crowdsourcing campaigns) and define and prioritize solution opportunities that can deliver real value to your business. We also apply techniques in operating model strategy to help you understand what else is needed to ensure a successful launch.
You select your top use cases then provide us with sample data and information about where it resides. We'll review your data in the context of your use case(s) BEFORE we ask you to engage with us. This de-risks your AI investment and simultaneously increases our confidence of you becoming a happy subscriber.
After we execute our Master Subscription and Services Agreement, we engage at the use case-level via Statements of Work. Each SOW will describe the use case to be enabled. It will also identify scope elements that you (or your services partner) will be responsible for. We ask for a retainer upon each SOW signature.
WeR.ai will enable the AI engines required by the use case and provide communications on work-stream progress. Project Management overall will be provided by you or your professional services partner. Upon engine completion (as evidenced by the use case working as specified) a milestone payment will be due to WeR.ai.
The subscription term will begin once the data science engines have enabled your use case and it is working as specified (exceptions to term start rules can apply). Once in production, the engines will be kept running (or learning) as a subscription service, backed up by our leading Service Level Agreements.
A popular approach to AI solution deployment is to launch a Minimally Viable Product and then, based on customer feedback and other lessons learned about solution and business performance, progress the solution along a product roadmap. As your data science engines partner, we will support you with roadmap planning.