Real-Time AI to Transform
Data to Insight

Our Singu̇AI platform puts YOU at the center of the AI revolution

  • Intelligent Document Processing
  • Intelligent Image Processing
  • Predictive Analytics


We make AI practical, scalable, and cost-effective

Fast. Accurate. Self-Evolving AI.

We produce faster, more accurate results with less sample sets than any other product in the market. Unlike other systems where there is no way to adapt to new data once the model for a particular document type is built, Singu̇AI never stops learning.

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 underlining Engines – Singu̇TXT, Singu̇IMG, and Singu̇PREDICT.

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.

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.

WHY US

Business Value and Advantages

Technology Leadership

World leading NLP technology, advanced adaptive image processing technology, 15 US patents pending.​

Adaptable & Resilient

One model supports multiple use cases / scenarios and has a wide range of applications across all functional departments.​

Fast & Easy

80% cost saving with rapid ROI. No data scientists or coding necessary. Customers can create, maintain & update models on their own.​

HOW IT WORKS

Model Training in 3 Easy Steps

01 Backbone Model

We utilize a pretrained platform that has been trained on massive amounts of data (imagine all of the text available online and every published document written) and can recognize any document language.

02 Transfer Learning

Human provides a small sample of labelled data to define model requirements

03 Model Adaptation

As data involves, human keeps providing updated small sample of labelled data. Model will adapt accordingly

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