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The operational issue or opportunity is defined in business/client terms. We avoid jumping into a technical solution discussion at this point. Our solution engineering methodology begins with a comprehensive understanding of drivers and objectives.

We use a suppliers, inputs, process, outputs, and customers (SIPOC) based process tool from our Six Sigma toolkit to fully rationalize the central process and requirements. This tool and method allow us to fully engage with our clients the key functions of their operations and minimizes any ambiguity in solution development and integration.

Computational models and algorithms are selected, refined and developed to address the central process to be augmented. We find that each client’s needs are unique when it comes to the precise models employed, and the resulting intelligent solutions end up processing truly dynamic intelligence engines.

With the to-be process fully defined at the functional level, we determine the type of artificial intelligence model that will drive the most accurate and intelligent results. This definition of a human-like intelligence solution that can learn, reason, plan, perceive, and process natural language appropriate to the central process significantly defines how the solution will work.

Our solution architects, data scientists, creative minds, and systems engineers design, code, integrate, and test the customized solution. The product of this phase in a process-specific intelligent technology that interprets rational behavior in human-like manner, depending on the specific process and method, is applied.

Like any organism, artificial intelligence requires progressive learning. SAI employs a variety of tools, frequently centered on neural networks that train themselves as more data is introduced. These AI engines enhance and create their own algorithms using weighing of neural relatedness as the system advances.