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An integrated workforce analytics platform powered by AI and machine learning.
Claims Management Solutions
Reduce denials, improve first-pass rates and cut admin burden.
Quality Assurance Solutions
Integrate sampling, capacity planning, comprehensive auditing, and rebuttals.

Contact Center
Solutions

Improve First Call Resolution with AI-enhanced integration.

How It Works

Integration

Privacy & Security

Proven it will
pay for itself in
the first year.
Our integrated workspaces are designed to help you get more done, faster.
Claims Management Features
Enhance productivity, AI-powered distribution, and production planning.
Quality Assurance Features
Improve First Call Resolution, call history and cross-department integration.

Contact Center
Features

Robust error feedback, rebuttal workflow, and error trend analysis.
  • Pre-Processing (Prepare) – All data undergoes preparation to improve its quality and suitability. The pre-processing step eliminates undesirable elements and enhances essential aspects of images, making them more compatible with AI processing. Tasks include – duplication removal, irrelevant data trim, filter of outliers, the identification of missing data, and the correction of structural errors.
  • Object Detection (Locating) – This stage involves the crucial task of pinpointing objects within images, achieved through image segmentation and determining object locations.
  • Object Recognition and Training (Labeling) – Deep Learning algorithms delve into images in this phase, extracting patterns and distinctive attributes associated with specific labels.
  • Object Classification (Applied) – This marks the peak of the process. At this stage, you’ve constructed an AI model capable of categorizing images based on various criteria and patterns learned from the training data.
  • Connecting Workflow (Integration) – You are now ready to integrate your image-classifying AI model into an AI workflow. This connection involves specifying the input source, where new data is obtained, and the corresponding output, which determines the actions taken once the data is classified.
  • Feedback Loop (Retraining) – To sustain effectiveness and relevance over time, the last step is to create a cyclical feedback loop that regularly collects operational process insights and incorporates them into the model’s learning process.