Chat-bot are consistently being adopted by most of the clients. Clients are concerned about the quality of the chat-bots and how they are supporting the end customer needs. Quality assurance of chat-bots are highly focused on dialog workflow. A complete quality assurance solutions should look beyond dialog workflow. It should focus on fairness, consistency, effectiveness of the chat-bots and look at the larger picture of where they fit in client applications. Full AI Cycle Testing (FACT) focuses on end to end application testing that includes chat-bots or conversation services as part of it. It evaluates the efficiency of chat-bot structure using static testing, focuses on sensitivity of the solution and also evaluates the payload of the conversation service and beyond. It brings in a scientific method to ensure 100% test coverage with optimal test cases thereby covering all dialog workflows as well. It uses deep neural networks to evaluate the sensitivity of the conversation solution.