Machine learning functionality can be embedded in applications to enhance and add new capabilities. The objective of integrating ML into enterprise software is to create a more predictable and data driven model that can be leveraged for automation, insights, and to make the business more competitive and profitable.
For example, NLP based surveys, chatbots, Search and Text Analytics can be leveraged to provide “quick wins” that provide measurable results. Predictive analytics can help in faster data driven decision making.
Primus has incorporated machine learning models and algorithms in applications for analysis and processing of documents, Search (Analytics), creating smart dashboards, and has built prototypes for fraud detection.
We can help clients from selecting use cases that deliver the best ROI to prototyping and building solutions utilizing machine learning platforms and engines in several commonly available cloud environments.
We design collaboratively. We develop transparently. We believe in agile as a process and a philosophy. Our team works with the Customer team as one; we deliver frequent functionally complete releases to ensure quick feedback and course correction.
Our process of enabling the customer to visualize the final product early on and weekly builds means there are no surprises.
Primus built an NLP based search module which was embedded in a document search application that was used to locate contracts and policy documents for our client, an insurance third party administrator. The search module also incorporated a prototype voice based search capability.
Document processing and categorization
Primus built a prototype application to process incoming documents in various formats, recognizing the category of document based on machine learning models, and then identifying specific types of information and populating data sets for further analysis and research. The ML models were trained with a finite set of documents and validated against a larger set.