Streamline proposal creation with the smartest AI, the best content, seamless integration with Microsoft Word, and unmatched efficiency.
Automate your best practices, processes, and standards to guide your proposal writers, sales teams, and subject experts. And don’t worry, it’s so easy to use they will use it. We would love the opportunity to help you quantify the impact your business can expect from investing in Expedience Software. Click here to request a Return on Investment (ROI) calculation. In this 15-minute session, we will ask 20 simple questions to assess and grade your current proposal quality and scalability. Manual proposal processes are likely costing you far more than you realize. These models waste time and kill the productivity of proposal writers, sales team members, senior staff, and subject experts.
Canopy is a cloud-based practice management software for accounting and tax firms, offering tools for client engagement, document management, workflow automation, and time & billing. Its Client Engagement platform centralizes interactions with a secure portal, customizable branding, and email integration, while the Document Management system enables organized, paperless file storage. The Workflow module enhances visibility into tasks and projects through templates, task assignments, and automation, reducing human error. Additionally, the Time & Billing feature tracks billable hours, generates invoices, and processes payments, ensuring accurate financial management. With its comprehensive features, Canopy streamlines operations, reduces stress, and enhances client experiences.
Non-disjoint groupping of Documents based on word sequence approach
This is a GUI for learning non disjoint groups of documents based on Weka machine learning framework. It offers the possibility to make non disjoint
clustering of documents using both vectorial and sequential representation (word sequence approach based on WSK kernel). All data format supported
by WEKA could be used in DocCO. Data could be loaded from files, from
databases or from specified URL. All the preprocessing techniques implemented in
WEKA could be used before performing the learning.