Business users work with our AI side-by-side to teach the AI how to process a given type of business document. The AI learns in real-time, trains and establishes its model in real-time, and makes predictions or inferences in real-time. The direct dialogue and interaction between the business users and the AI models cuts out the “middleman” to allow the business subject matter experts to establish the model themselves, guided by the AI. This approach avoids the messages lost in translation between the business group and the data scientists. It allows business users to focus on the business use cases and their value, while leaving the underlining technologies to the Singu̇AI Platform and its three underlying Engines – Singu̇TXT, Singu̇IMG, and Singu̇PREDICT.
The Singu̇AI platform provides feedback to business users to verify questionable results or process new streams of data to keep up with the dynamic changes in the real-world data streams. The variance may come in formats, layouts, length, or new information entered, etc. An example would be if you automated Accounts Payable and added a new vendor with a unique document layout. With Real-Time AI, business users are always included in the loop, but at non-technical, manageable and minimal levels, to keep up with the changes in the production data streams. This allows Singu̇AI to uniquely address one of the most significant challenges AI implementation: usually, once the models are built and deployed they are static and cannot adjust to changes or variances in the new data, and thus become less accurate over time.
Singu̇AI never stops learning.
Data scientists are in high demand and short supply as enterprises move forward rapidly with AI technologies to accelerate their business growth. Singu̇AI is a powerful platform with AutoML fully embedded to enable supervised learning. The three Engines that comprise Singu̇AI offer user-friendly interfaces that are designed to be operated by business users directly, without data scientists or programmers. With the full pipeline automated to optimize the data management and modeling, the deployment only involves API connections to the input and output of the data streams, making it easy to implement and maintain in production environments.
Business users can start with small amount of sample data to establish the Real-Time AI models in the matter of hours or days. This allows them to quickly validate their business use cases and make adjustments early. It helps them overcome one of the biggest challenges in AI implementation: it usually takes weeks to label enough data to start the first round of AI modeling, and it takes multiple iterations to reach the satisfactory level of accuracy to meet the business need. With much shortened label-training-testing life cycle, the business users can quickly establish the trend of the model involvement over time, and rapidly improve the accuracy with accelerated labelled data becoming available.