Case Study

Discover How CriticalRiver's AI Innovation Increases Operational Efficiency by 30%

Modern businesses actively seek to address organizational hurdles in communication, collaboration, and coordination through established practices. Although creating effective machine learning models is a common practice to tackle these challenges, it only solves part of the equation. MLOps emerges as a solution for enterprises to ensure agility and speed, crucial elements in today’s digital landscape, while effectively addressing challenges.

While handling data presents its set of tasks, deploying machine learning models to production introduces another layer of complexity. Data Scientists often create impactful models, yet deploying and utilizing them presents substantial challenges. Concurrently, Data Engineers and ML Engineers are consistently exploring innovative methods for deploying their machine learning models in a production environment.

CriticalRiver partnered with a data engineering and AI/ML platform to create Momentum-powered RESTful APIs for Signature Matching, driven by AI. This solution encompasses two integral processes: signature extraction and matching. In the extraction phase, signatures are detected and their positions in a document are identified. Users initiate the secure extraction API by submitting a scanned image, and in return, receive information on the locations and confidence scores of all detected signatures. The matching process involves identifying similarities between two signature images, eliminating background noise, resizing signatures, and correcting their orientation.

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